blood plasma lipidomics - Data analysis (-)

Load libraries

library(readxl) # for reading in excel files
library(janitor) # data checks and cleaning
library(glue) # for easy pasting
library(FactoMineR) # for PCA
library(factoextra) # for PCA
library(rstatix) # for stats
library(pheatmap) # for heatmaps
library(plotly) # for interactive plots
library(htmlwidgets) # for saving interactive plots
library(devtools)
library(notame) # used for feature clustering
library(doParallel)
library(igraph) # feature clustering
library(ggpubr) # visualizations
library(knitr) # clean table printing
library(rmarkdown)
library(corrr)
library(ggcorrplot)
library(ggthemes)
library(ggtext)
library(PCAtools)
library(pathview) # for functional analysis and KEGG annotation
library(mixOmics) #multilevel PCA and sPLS-DA
library(tidyverse) # for everything

Read in data

# raw filtered metabolomics data 
omicsdata <- read_csv("features_post-data-filtering.csv") %>%
  clean_names()

# metadata
metadata <- read_csv("../../../metadata-new.csv")

# carotenoids data
carotenoids <- read_excel("../../../Plasma carot_ret_apo levels.xlsx",
                          sheet = "B1andB3") %>%
  clean_names()

Wrangle data

Metadata

carotenoids <- carotenoids %>%
  mutate(total_carotenoids = b_c_nmol_l_plasma + lyc_nmol_l_plasma + retinol_nmol_l_plasma + apo13one_nmol_l_plasma)

Feature data

# how many features
nrow(omicsdata)

## [1] 1043

# are there any duplicates?
omicsdata %>% get_dupes(mz_rt)

## # A tibble: 4 × 89
##   mz_rt          dupe_count row_id x5101_b1_control_c18…¹ x5105_b3_lyc_c18neg_21
##   <chr>               <int>  <dbl>                  <dbl>                  <dbl>
## 1 724.5276_8.3            2   2843                  4697.                  8713.
## 2 724.5276_8.3            2   2846                  4697.                  8713.
## 3 863.6765_10.6…          2   3900                 17536.                 12167.
## 4 863.6765_10.6…          2   3903                 17536.                 12167.
## # ℹ abbreviated name: ¹​x5101_b1_control_c18neg_82
## # ℹ 84 more variables: x5112_b1_beta_c18neg_34 <dbl>,
## #   x5107_b1_beta_c18neg_29 <dbl>, x5105_b1_lyc_c18neg_76 <dbl>,
## #   x5102_b1_control_c18neg_5 <dbl>, x5101_b3_control_c18neg_66 <dbl>,
## #   x5103_b1_lyc_c18neg_35 <dbl>, x5104_b3_lyc_c18neg_32 <dbl>,
## #   x5109_b3_beta_c18neg_81 <dbl>, x5104_b1_lyc_c18neg_10 <dbl>,
## #   x5108_b1_control_c18neg_71 <dbl>, x5113_b3_beta_c18neg_47 <dbl>, …

# remove dupes
omicsdata <- omicsdata %>% 
  distinct(mz_rt, .keep_all = TRUE)

# check again for dupes
omicsdata %>% get_dupes(mz_rt)

## # A tibble: 0 × 89
## # ℹ 89 variables: mz_rt <chr>, dupe_count <int>, row_id <dbl>,
## #   x5101_b1_control_c18neg_82 <dbl>, x5105_b3_lyc_c18neg_21 <dbl>,
## #   x5112_b1_beta_c18neg_34 <dbl>, x5107_b1_beta_c18neg_29 <dbl>,
## #   x5105_b1_lyc_c18neg_76 <dbl>, x5102_b1_control_c18neg_5 <dbl>,
## #   x5101_b3_control_c18neg_66 <dbl>, x5103_b1_lyc_c18neg_35 <dbl>,
## #   x5104_b3_lyc_c18neg_32 <dbl>, x5109_b3_beta_c18neg_81 <dbl>,
## #   x5104_b1_lyc_c18neg_10 <dbl>, x5108_b1_control_c18neg_71 <dbl>, …

# how many features
nrow(omicsdata)

## [1] 1041

Sometimes a weird logical column (lgl) comes up in my data. Let’s check if it’s there

colnames(omicsdata)

##  [1] "mz_rt"                      "row_id"                    
##  [3] "x5101_b1_control_c18neg_82" "x5105_b3_lyc_c18neg_21"    
##  [5] "x5112_b1_beta_c18neg_34"    "x5107_b1_beta_c18neg_29"   
##  [7] "x5105_b1_lyc_c18neg_76"     "x5102_b1_control_c18neg_5" 
##  [9] "x5101_b3_control_c18neg_66" "x5103_b1_lyc_c18neg_35"    
## [11] "x5104_b3_lyc_c18neg_32"     "x5109_b3_beta_c18neg_81"   
## [13] "x5104_b1_lyc_c18neg_10"     "x5108_b1_control_c18neg_71"
## [15] "x5113_b3_beta_c18neg_47"    "x5111_b1_control_c18neg_4" 
## [17] "x5109_b1_beta_c18neg_65"    "x5111_b3_control_c18neg_39"
## [19] "x5110_b1_lyc_c18neg_59"     "x5102_b3_control_c18neg_33"
## [21] "x5108_b3_control_c18neg_22" "x5103_b3_lyc_c18neg_63"    
## [23] "x5107_b3_beta_c18neg_28"    "x5112_b3_beta_c18neg_56"   
## [25] "x5113_b1_beta_c18neg_53"    "x5110_b3_lyc_c18neg_84"    
## [27] "x5114_b1_beta_c18neg_26"    "x5114_b3_beta_c18neg_23"   
## [29] "x5122_b3_lyc_c18neg_75"     "x5124_b3_control_c18neg_68"
## [31] "x5117_b3_lyc_c18neg_2"      "x5120_b1_beta_c18neg_72"   
## [33] "x5115_b1_lyc_c18neg_36"     "x5125_b3_control_c18neg_51"
## [35] "x5116_b3_control_c18neg_20" "x5126_b3_beta_c18neg_24"   
## [37] "x5118_b3_lyc_c18neg_9"      "x5117_b1_lyc_c18neg_46"    
## [39] "x5123_b3_control_c18neg_40" "x5115_b3_lyc_c18neg_70"    
## [41] "x5122_b1_lyc_c18neg_14"     "x5125_b1_control_c18neg_80"
## [43] "x5123_b1_control_c18neg_11" "x5116_b1_control_c18neg_6" 
## [45] "x5120_b3_beta_c18neg_54"    "x5121_b1_beta_c18neg_69"   
## [47] "x5121_b3_beta_c18neg_48"    "x5118_b1_lyc_c18neg_57"    
## [49] "x5124_b1_control_c18neg_45" "x5119_b3_control_c18neg_58"
## [51] "x5119_b1_control_c18neg_74" "x5127_b1_control_c18neg_30"
## [53] "x5131_b1_beta_c18neg_38"    "x5135_b1_lyc_c18neg_12"    
## [55] "x5129_b3_lyc_c18neg_16"     "x5131_b3_beta_c18neg_3"    
## [57] "x5134_b3_beta_c18neg_64"    "x5133_b3_control_c18neg_15"
## [59] "x5127_b3_control_c18neg_83" "x5126_b1_beta_c18neg_62"   
## [61] "x5136_b1_beta_c18neg_44"    "x5133_b1_control_c18neg_27"
## [63] "x5134_b1_beta_c18neg_42"    "x5135_b3_lyc_c18neg_18"    
## [65] "x5128_b1_lyc_c18neg_8"      "x5128_b3_lyc_c18neg_60"    
## [67] "x5136_b3_beta_c18neg_77"    "x5132_b1_beta_c18neg_41"   
## [69] "x5132_b3_beta_c18neg_52"    "x5129_b1_lyc_c18neg_78"    
## [71] "x5130_b3_lyc_c18neg_50"     "x5130_b1_lyc_c18neg_17"    
## [73] "qc2_c18neg_7"               "qc1_c18neg_1"              
## [75] "qc3_c18neg_13"              "qc4_c18neg_19"             
## [77] "qc5_c18neg_25"              "qc7_c18neg_37"             
## [79] "qc8_c18neg_43"              "qc6_c18neg_31"             
## [81] "qc14_c18neg_79"             "qc13_c18neg_73"            
## [83] "qc12_c18neg_67"             "qc10_c18neg_55"            
## [85] "qc15_c18neg_85"             "qc11_c18neg_61"            
## [87] "qc9_c18neg_49"              "x88"

# remove lgl column
omicsdata <- omicsdata %>%
  dplyr::select(!where(is.logical)) 

colnames(omicsdata)

##  [1] "mz_rt"                      "row_id"                    
##  [3] "x5101_b1_control_c18neg_82" "x5105_b3_lyc_c18neg_21"    
##  [5] "x5112_b1_beta_c18neg_34"    "x5107_b1_beta_c18neg_29"   
##  [7] "x5105_b1_lyc_c18neg_76"     "x5102_b1_control_c18neg_5" 
##  [9] "x5101_b3_control_c18neg_66" "x5103_b1_lyc_c18neg_35"    
## [11] "x5104_b3_lyc_c18neg_32"     "x5109_b3_beta_c18neg_81"   
## [13] "x5104_b1_lyc_c18neg_10"     "x5108_b1_control_c18neg_71"
## [15] "x5113_b3_beta_c18neg_47"    "x5111_b1_control_c18neg_4" 
## [17] "x5109_b1_beta_c18neg_65"    "x5111_b3_control_c18neg_39"
## [19] "x5110_b1_lyc_c18neg_59"     "x5102_b3_control_c18neg_33"
## [21] "x5108_b3_control_c18neg_22" "x5103_b3_lyc_c18neg_63"    
## [23] "x5107_b3_beta_c18neg_28"    "x5112_b3_beta_c18neg_56"   
## [25] "x5113_b1_beta_c18neg_53"    "x5110_b3_lyc_c18neg_84"    
## [27] "x5114_b1_beta_c18neg_26"    "x5114_b3_beta_c18neg_23"   
## [29] "x5122_b3_lyc_c18neg_75"     "x5124_b3_control_c18neg_68"
## [31] "x5117_b3_lyc_c18neg_2"      "x5120_b1_beta_c18neg_72"   
## [33] "x5115_b1_lyc_c18neg_36"     "x5125_b3_control_c18neg_51"
## [35] "x5116_b3_control_c18neg_20" "x5126_b3_beta_c18neg_24"   
## [37] "x5118_b3_lyc_c18neg_9"      "x5117_b1_lyc_c18neg_46"    
## [39] "x5123_b3_control_c18neg_40" "x5115_b3_lyc_c18neg_70"    
## [41] "x5122_b1_lyc_c18neg_14"     "x5125_b1_control_c18neg_80"
## [43] "x5123_b1_control_c18neg_11" "x5116_b1_control_c18neg_6" 
## [45] "x5120_b3_beta_c18neg_54"    "x5121_b1_beta_c18neg_69"   
## [47] "x5121_b3_beta_c18neg_48"    "x5118_b1_lyc_c18neg_57"    
## [49] "x5124_b1_control_c18neg_45" "x5119_b3_control_c18neg_58"
## [51] "x5119_b1_control_c18neg_74" "x5127_b1_control_c18neg_30"
## [53] "x5131_b1_beta_c18neg_38"    "x5135_b1_lyc_c18neg_12"    
## [55] "x5129_b3_lyc_c18neg_16"     "x5131_b3_beta_c18neg_3"    
## [57] "x5134_b3_beta_c18neg_64"    "x5133_b3_control_c18neg_15"
## [59] "x5127_b3_control_c18neg_83" "x5126_b1_beta_c18neg_62"   
## [61] "x5136_b1_beta_c18neg_44"    "x5133_b1_control_c18neg_27"
## [63] "x5134_b1_beta_c18neg_42"    "x5135_b3_lyc_c18neg_18"    
## [65] "x5128_b1_lyc_c18neg_8"      "x5128_b3_lyc_c18neg_60"    
## [67] "x5136_b3_beta_c18neg_77"    "x5132_b1_beta_c18neg_41"   
## [69] "x5132_b3_beta_c18neg_52"    "x5129_b1_lyc_c18neg_78"    
## [71] "x5130_b3_lyc_c18neg_50"     "x5130_b1_lyc_c18neg_17"    
## [73] "qc2_c18neg_7"               "qc1_c18neg_1"              
## [75] "qc3_c18neg_13"              "qc4_c18neg_19"             
## [77] "qc5_c18neg_25"              "qc7_c18neg_37"             
## [79] "qc8_c18neg_43"              "qc6_c18neg_31"             
## [81] "qc14_c18neg_79"             "qc13_c18neg_73"            
## [83] "qc12_c18neg_67"             "qc10_c18neg_55"            
## [85] "qc15_c18neg_85"             "qc11_c18neg_61"            
## [87] "qc9_c18neg_49"

Tidy

# create long df for omics df
omicsdata_tidy <- omicsdata %>%
  pivot_longer(cols = 3:ncol(.),
               names_to = "sample",
               values_to = "peak_height") %>%
  mutate(sample2 = sample) %>%
  # add a new column with just subject
  rename("subject" = sample2) %>%
  # remove the suffix from subject names
  mutate_at("subject", str_sub, start=2, end=5)

Combine dfs

## tibble [88,485 × 18] (S3: tbl_df/tbl/data.frame)
##  $ subject          : chr [1:88485] "5101" "5105" "5112" "5107" ...
##  $ sample           : chr [1:88485] "x5101_b1_control_c18neg_82" "x5105_b3_lyc_c18neg_21" "x5112_b1_beta_c18neg_34" "x5107_b1_beta_c18neg_29" ...
##  $ treatment        : chr [1:88485] "control" "red" "beta" "beta" ...
##  $ tomato_or_control: chr [1:88485] "control" "tomato" "tomato" "tomato" ...
##  $ mz               : num [1:88485] 273 273 273 273 273 ...
##  $ rt               : num [1:88485] 0.485 0.485 0.485 0.485 0.485 0.485 0.485 0.485 0.485 0.485 ...
##  $ row_id           : num [1:88485] 62 62 62 62 62 62 62 62 62 62 ...
##  $ peak_height      : num [1:88485] 11987 15548 14778 13083 14824 ...
##  $ sex              : chr [1:88485] "F" "M" "F" "M" ...
##  $ bmi              : num [1:88485] 29.4 33.5 26 23.8 33.5 ...
##  $ age              : num [1:88485] 55 61 60 68 61 62 55 50 37 65 ...
##  $ tot_chol         : num [1:88485] 235 189 235 177 189 229 235 172 203 127 ...
##  $ ldl_chol         : num [1:88485] 150.4 125.6 149.2 85.4 125.6 ...
##  $ hdl_chol         : num [1:88485] 71 45 52 79 45 40 71 53 31 40 ...
##  $ triglycerides    : num [1:88485] 68 92 169 63 92 184 68 38 125 88 ...
##  $ glucose          : num [1:88485] 90 103 92 102 103 94 90 92 93 87 ...
##  $ SBP              : num [1:88485] 156 131 107 122 131 101 156 106 100 147 ...
##  $ DBP              : num [1:88485] 90 78 70 58 78 62 90 71 67 76 ...

# replace NA's in certain columns with QC
meta_omics_sep$subject <- str_replace_all(meta_omics_sep$subject, "c", "qc")
  

meta_omics_sep$sample <- meta_omics_sep$sample %>%
  replace_na("QC")

meta_omics_sep$treatment <- meta_omics_sep$treatment %>%
  replace_na("QC")

meta_omics_sep$tomato_or_control <- meta_omics_sep$tomato_or_control %>%
  replace_na("QC")

Data summaries

Number of masses detected

nrow(omicsdata)

## [1] 1041

Mass range for metabolites detected?

range(meta_omics_sep$mz)

## [1]   89.0243 1697.1555

RT range for metabolites detected?

range(meta_omics_sep$rt)

## [1]  0.485 11.170

Mass vs RT scatterplot

# plot
(plot_mzvsrt <- meta_omics_sep %>%
  ggplot(aes(x = rt, y = mz)) +
  geom_point() +
  theme_minimal() +
  labs(x = "Retention time, min",
       y = "m/z",
       title = "mz across RT for all features"))

Histogram for mass range

meta_omics_sep %>%
  group_by(mz) %>%
  ggplot(aes(x = mz)) +
  geom_histogram(binwidth = 25) +
  theme_minimal() +
  labs(x = "Monoisotopic mass (amu)",
       y = "Number of features",
       title = "Distribution of features by mass")

Histogram for RT

meta_omics_sep %>%
  group_by(mz) %>%
  ggplot(aes(x = rt)) +
  geom_histogram(binwidth = 0.1) + # 6 second bins
  theme_minimal() +
  labs(x = "Retention time",
       y = "Number of features",
       title = "Distribution of features by retention time")

NAs and imputing

NAs

# samples only (no QCs)
omicsdata_noQC <- omicsdata %>%
  dplyr::select(-contains("qc"))

#NAs in samples only?
NAbyRow_noQC <- rowSums(is.na(omicsdata_noQC[,-1]))

hist(NAbyRow_noQC,
     breaks = 70, # because there are 70 samples 
     xlab = "Number of missing values",
     ylab = "Number of metabolites",
     main = "How many missing values are there?")

Are there any missing values in QCs? There shouldn’t be after data preprocessing/filtering

omicsdata_QC <- omicsdata %>%
  dplyr::select(starts_with("qc")) 

NAbyRow_QC <- colSums(is.na(omicsdata_QC))
# lets confirm that there are no missing values from my QCs
sum(NAbyRow_QC) # no

## [1] 0

# calculate how many NAs there are per feature in whole data set
contains_NAs <- meta_omics %>%
  group_by(mz_rt) %>%
  count(is.na(peak_height)) %>%
  filter(`is.na(peak_height)` == TRUE)
kable(contains_NAs)

mz_rt	is.na(peak_height)	n
1315.8467_3.079	TRUE	52
137.0244_0.741	TRUE	6
151.0261_0.612	TRUE	15
1602.9935_2.726	TRUE	2
1604.0878_6.358	TRUE	3
1605.0902_6.358	TRUE	1
1668.2129_8.463	TRUE	1
1669.2155_8.46	TRUE	1
187.0515_0.642	TRUE	7
191.1076_0.694	TRUE	66
201.0226_0.688	TRUE	12
229.0537_0.703	TRUE	5
230.9967_0.644	TRUE	6
231.0795_0.662	TRUE	32
235.0972_0.69	TRUE	63
241.0868_1.048	TRUE	61
255.0871_0.663	TRUE	1
257.0795_0.669	TRUE	52
326.0874_0.626	TRUE	63
352.0856_0.687	TRUE	2
391.2849_2.246	TRUE	5
397.1499_0.653	TRUE	64
411.1294_0.644	TRUE	66
421.1543_0.701	TRUE	68
437.0542_0.688	TRUE	15
437.2904_2.11	TRUE	7
448.3062_1.477	TRUE	1
449.254_2.573	TRUE	1
453.285_1.371	TRUE	47
462.1763_0.655	TRUE	5
464.3013_0.999	TRUE	18
473.3474_4.696	TRUE	47
507.223_2.273	TRUE	2
514.2836_0.69	TRUE	31
517.2436_2.68	TRUE	60
559.4722_4.208	TRUE	1
624.3379_0.689	TRUE	1
624.3382_0.91	TRUE	19
784.549_6.62	TRUE	27
800.5587_8.868	TRUE	1
818.5544_4.825	TRUE	38
830.5005_2.723	TRUE	6
881.5332_7.243	TRUE	4

Remove NAs

There are some features that are missing in a majority of subjects. I’m going to remove those because they may skew the data.

# number of features
nrow(omicsdata)

## [1] 1041

# Removing features missing from over 90% of data
omit_features <- contains_NAs %>%
  filter(n/70 >= 0.90)

#preview
nrow(omit_features) # features to remove

## [1] 6

# how many features should be left?
nrow(omicsdata) - nrow(omit_features)

## [1] 1035

# now remove these features from the omics dataset
omicsdata <- omicsdata %>%
  anti_join(omit_features,
            by = "mz_rt")

 # check number of features now?
nrow(omicsdata)

## [1] 1035

Data imputation

# impute any missing values by replacing them with 1/2 of the lowest peak height value of a feature (i.e. in a row).
imputed_omicsdata <- omicsdata

imputed_omicsdata[] <- lapply(imputed_omicsdata, 
                              function(x) ifelse(is.na(x),
                                                 min(x, na.rm = TRUE)/2, x))

dim(imputed_omicsdata)

## [1] 1035   87

Are there any NAs?

imputed_omicsdata %>%
  is.na() %>%
  sum()

## [1] 0

# imputations worked

New tidy df

# create long df for imputed omics df
imputed_omicsdata_tidy <- imputed_omicsdata %>%
  pivot_longer(cols = 3:ncol(.),
               names_to = "sample",
               values_to = "peak_height") %>%
  mutate(sample2 = sample) %>%
  # add a new column with just subject
  rename("subject" = sample2) %>%
  # remove the suffix from subject names
  mutate_at("subject", str_sub, start=2, end=5) 

# combine meta and imputed omics dfs
imp_meta_omics <- full_join(imputed_omicsdata_tidy,
                         metadata,
                         by = c("subject" = "subject"))

# separate mz and rt
imp_meta_omics_sep <- imp_meta_omics %>%
  separate(col = mz_rt,
           into = c("mz", "rt"),
           sep = "_") 

# convert columns to correct type
imp_meta_omics_sep$mz <- as.numeric(imp_meta_omics_sep$mz)
imp_meta_omics_sep$rt <- as.numeric(imp_meta_omics_sep$rt)

Notame feature reduction

vignette for reference

#browseVignettes("notame")

Let’s look at what masses come up at each RT again

# rt vs mz plot
imp_meta_omics_sep %>%
  ggplot(aes(x = rt, y = mz)) +
  geom_point() +
  theme_minimal() +
  labs(x = "RT (min)",
       y = "mz")

There are some points that are at the same RT, meaning they could be coming from the same compound. We’ll run notame clustering to collapse features coming from one mass into one feature.

Data restructuring for notame

# create features list from imputed data set to only include unique feature ID's (mz_rt), mz and RT
features <- imp_meta_omics_sep %>%
  cbind(imp_meta_omics$mz_rt) %>%
  rename("mz_rt" = "imp_meta_omics$mz_rt") %>%
  dplyr::select(c(mz_rt, mz, rt)) %>%
  distinct() # remove the duplicate rows

# create a second data frame which is just imp_meta_omics restructured to another wide format
data_notame <- data.frame(imputed_omicsdata %>%
                            dplyr::select(-row_id) %>%
                            t())

data_notame <- data_notame %>%
  tibble::rownames_to_column() %>% # change samples from rownames to its own column
  row_to_names(row_number = 1) # change the feature IDs (mz_rt) from first row obs into column names

Check structures

# check if mz and rt are numeric
str(features)

## 'data.frame':    1035 obs. of  3 variables:
##  $ mz_rt: chr  "272.9587_0.485" "288.9363_0.485" "226.9658_0.499" "294.9532_0.5" ...
##  $ mz   : num  273 289 227 295 363 ...
##  $ rt   : num  0.485 0.485 0.499 0.5 0.501 0.514 0.514 0.515 0.515 0.515 ...

# check if results are numeric
head(data_notame, n = 1)

##                        mz_rt 272.9587_0.485 288.9363_0.485 226.9658_0.499
## 2 x5101_b1_control_c18neg_82     11986.5490     21534.8980     28760.8710
##   294.9532_0.5 362.9406_0.501 520.9099_0.514 588.8973_0.514 452.9225_0.515
## 2   22338.5640     34403.1300    114667.8360     98069.3900    102553.1200
##   656.8848_0.515 724.8722_0.515 792.8596_0.515 384.9351_0.516 316.9477_0.518
## 2     59991.9960     46757.6500     28809.4430     97462.6900    129814.3400
##   604.8712_0.518 248.9604_0.52 215.0328_0.588 217.0297_0.589 151.0261_0.612
## 2     43247.6520   139909.1600    423488.8000    147959.5300      1008.0625
##   335.0471_0.599 649.1192_0.601 167.021_0.602 665.0885_0.607 643.1067_0.611
## 2     89597.6100     17832.4840   169550.3600     17469.2250     21769.1270
##   627.1374_0.611 329.0355_0.613 621.1249_0.613 459.1113_0.613 935.1964_0.613
## 2     35917.1130     44170.4260     19929.6100     12582.7990      6787.0806
##   919.2277_0.616 605.1555_0.616 313.0652_0.617 291.0839_0.618 128.0352_0.617
## 2      9664.9620     76629.0500    232746.4500   1622794.8000     35370.3630
##   583.1735_0.618 247.0933_0.62 293.0879_0.621 191.0197_0.621 111.0086_0.62
## 2     16841.3160    30654.0470     35340.3240    643951.4000   150325.2200
##   950.1575_0.623 636.085_0.627 658.0669_0.628 680.0486_0.63 389.052_0.635
## 2      3818.7810    17712.3800     17415.8850     6436.0660    23057.9500
##   263.1035_0.638 277.0734_0.639 343.9948_0.639 172.9913_0.642 187.0418_0.642
## 2     66218.5000     41315.5550    216509.7300    115296.5800    209861.0800
##   187.0515_0.642 345.9989_0.642 300.0047_0.643 201.0575_0.644 188.9862_0.644
## 2     14098.1120      6601.0000     23712.5230     48690.5860      9552.8840
##   89.0243_0.645 230.9967_0.644 93.0344_0.646 178.0509_0.647 479.0972_0.649
## 2   239368.3800      1436.5206    31256.6620     23041.9240     31571.9530
##   103.04_0.651 462.1763_0.655 187.007_0.66 203.0013_0.66 107.0502_0.661
## 2   48823.0800      3000.7876  600991.2000     7830.9595    110424.4450
##   194.0457_0.66 255.0871_0.663 383.1529_0.667 187.0165_0.665 117.0557_0.669
## 2     3220.5076      8777.2920      1042.2191     16411.9530     56197.1560
##   291.0953_0.67 245.0486_0.685 352.0856_0.687 201.0226_0.688 413.1998_0.688
## 2    15675.7380     17020.8540      8563.7650      1560.3474      6060.3726
##   437.0542_0.688 510.2523_0.688 624.3379_0.689 514.2836_0.69 213.0223_0.689
## 2      1315.9800      6418.2686      6967.8380      504.0312      3920.3600
##   397.1781_0.69 129.0557_0.69 367.1581_0.691 257.0795_0.669 528.2631_0.691
## 2    13469.2600   126977.6400     36765.0980       504.0312      7024.1000
##   231.0795_0.662 397.2049_0.693 229.0537_0.703 244.908_0.706 246.9051_0.707
## 2      1033.3367     10682.8890      1605.1570    28453.5800     26781.3090
##   369.1735_0.722 137.0244_0.741 624.3382_0.91 239.0923_0.975 464.3013_0.999
## 2     16647.0840      2228.7122     2404.1950     13168.3100       504.0312
##   241.0868_1.048 448.3063_1.326 453.285_1.371 583.2555_1.358 448.3062_1.477
## 2       504.0312      2249.9277      504.0312      8213.1480      2152.3337
##   267.1235_1.539  507.223_1.7 437.2904_2.11 586.3143_2.221 391.2849_2.246
## 2     14312.3720   28249.8280     2109.4116      7916.0166      7834.5107
##   562.3143_2.27 507.223_2.273 297.1527_2.281 512.2988_2.28 311.2224_2.465
## 2     6131.5140     5463.4160      4852.7866    18104.6400      4982.1772
##   538.3144_2.488 449.254_2.573 612.33_2.594 640.2923_2.673 656.3173_2.673
## 2     26836.0620     3670.2560   27236.7710      8631.6280     11211.9810
##   578.3011_2.673 588.3302_2.677 526.3144_2.683 311.1683_2.682 1107.6619_2.7
## 2     11934.0640    156921.7200     14725.9940     10671.6130     6479.0815
##   517.2436_2.68 524.2776_2.71 830.5005_2.723 1151.649_2.727 504.3089_2.726
## 2      504.0312    17717.5160      1502.1267      2805.0450     18348.5370
##   564.3308_2.727 1135.6242_2.73 556.299_2.728 694.2877_2.728 1083.6621_2.728
## 2    396449.9700      2101.6200    10870.7270     11570.6170      15289.0240
##   616.2924_2.728 554.3012_2.728 1073.6331_2.728 632.3174_2.729 1602.9935_2.726
## 2     20911.8340     28204.3280       3184.2246     25367.3900       1053.2416
##   619.2886_2.757 500.2777_2.802 614.3455_2.824 476.2777_2.847 590.3457_2.989
## 2     12485.9420     35047.2400      7444.0780     27902.5570     39904.7900
##   1521.9681_3.075 670.2878_3.073 1165.6197_3.074 532.2989_3.075 530.3013_3.075
## 2       2808.4710     21550.0350       6353.8237     23154.2200     62886.2770
##   1025.6334_3.075 592.2925_3.075 540.3313_3.076 608.3174_3.076 1035.6627_3.076
## 2      12987.2740     42669.5270   1157627.5000     52637.2970      93593.6800
##   654.31_3.076 1103.6493_3.076 480.309_3.076 1087.6246_3.076 1531.9972_3.076
## 2    8150.1400      14690.8430    47245.8500      10563.3290       7595.6387
##   1520.9647_3.076 1027.6336_3.076 1530.994_3.076 1598.9806_3.076
## 2       3523.7397       6002.6210     10113.0330       2481.0215
##   1315.8467_3.079 820.5155_3.079 1061.6776_3.089 452.2777_3.197 618.308_3.232
## 2        504.0312      2857.1274       7589.4805     23911.5370    14584.3490
##   634.3329_3.232 506.3245_3.232 566.3462_3.232 556.3168_3.233 696.3033_3.233
## 2     20147.4020     10277.2140    273539.3400     25799.9820      7031.4870
##   1087.6933_3.233 558.3148_3.234 478.2933_3.354 524.3351_3.376 526.3505_3.4
## 2       7558.6090      8811.2780     16215.1490     21914.5800   18850.2320
##   592.3612_3.407 554.3456_3.441 436.2827_3.522 552.3663_3.532 698.3189_3.775
## 2      7309.2260     20630.9320     13906.4640     11245.3920      8278.1290
##   1159.7117_3.776 1081.6957_3.777 508.3402_3.777 568.3621_3.777 620.3237_3.777
## 2       4318.5205       5479.5150     16299.1320    532299.4400     24095.6450
##   1143.6869_3.778 636.3486_3.777 558.3325_3.778 1091.7248_3.778 626.3198_3.778
## 2       3467.0476     39472.6130     42671.3800      21538.7910     10507.0020
##   560.3303_3.778 1615.0859_3.778 594.3768_3.858 480.309_3.883 599.3194_3.921
## 2     14168.7450       2119.9607      6830.7280    45650.4400     15658.0970
##   583.2554_3.939 605.2373_3.94 1167.518_3.94 554.3818_4.037 414.0583_4.043
## 2     23206.5250     6507.5527     5111.5010      7755.4146      8064.4450
##   253.2169_4.044 327.2325_4.055 511.3996_4.087 464.314_4.127 303.2326_4.149
## 2      3715.8516     40238.2850      3120.4510    17825.3830     43368.2270
##   440.0739_4.207 442.071_4.208 279.2327_4.208 444.0682_4.208 559.4722_4.208
## 2     56946.7030    46998.2070    129523.0160     17429.4550      8758.2900
##   463.3422_4.219 537.4153_4.24 539.4304_4.266 539.4306_4.266 573.4515_4.266
## 2      4484.4497    33903.0470     13237.3580     13237.3580     11812.4580
##   445.3316_4.28 467.3735_4.306 591.462_4.314 445.3316_4.415 539.4309_4.416
## 2    16675.6540     24954.8000     5188.5474     22877.8380     10927.5510
##   511.3997_4.453 447.3471_4.499 493.389_4.514 447.3471_4.631 473.3629_4.632
## 2      3652.2422     11653.1750     5953.3700     11625.6970      5888.9800
##   567.4621_4.643 446.0838_4.646 444.0866_4.647 281.2484_4.648 442.0896_4.647
## 2      4465.4155     19138.6820     50135.1560    200246.7800     51583.4450
##   563.5036_4.648 591.462_4.651 449.3627_4.663 593.4778_4.664 575.467_4.662
## 2     23532.0410     6583.1655      9165.3930     35374.8830     3355.5427
##   473.3474_4.696 425.363_4.712 441.3942_4.718 491.3733_4.784 818.5544_4.825
## 2       504.0312    12562.7590      4651.2950      4547.7983       504.0312
##   551.3582_4.841 573.4515_4.873 561.3787_4.908 577.3738_4.918 617.4753_4.956
## 2      1659.1428      5616.5020      8191.6600      5274.4893      3398.3782
##   595.4933_4.953 691.5022_4.977 495.4046_4.993   397.3681_5 717.5179_5.106
## 2     23644.1680     11188.6350      4821.5300    6125.1910     27211.0740
##   465.3192_5.112 465.3038_5.114 591.3894_5.174 705.5178_5.308 479.3193_5.338
## 2      8263.3955    110951.5100     11246.6020      6977.3240      6625.6714
##   575.4672_5.354 411.3837_5.349 605.4051_5.422 579.3893_5.557 878.584_5.681
## 2      3909.9460      7838.0220      6459.4814      2674.2950     4946.8696
##   1394.0685_5.684 787.5209_5.683 709.5047_5.684 719.534_5.684 771.4961_5.685
## 2       4091.1677     22534.1900     23499.2420   222421.5600     10974.9320
##   711.5035_5.685 849.4911_5.685 493.3349_5.699 722.4967_5.728 904.599_5.836
## 2      9168.9520      6182.0674     10544.2640      3234.9639     6464.1490
##   1447.1028_5.846 1446.0998_5.845 875.5066_5.845 735.5203_5.846 813.5365_5.846
## 2       8393.4350      10199.0020      9896.5600     37915.4380     33982.0820
##   737.5193_5.846 745.5498_5.846 859.5292_5.846 803.5076_5.847 797.5117_5.847
## 2     15724.5500    381718.6600      6394.4097     11439.6400     18180.0400
##   1271.7465_5.862 848.5434_5.871 822.5279_5.875 748.5122_5.882 824.5433_5.925
## 2       9264.0960      5360.7744      6334.9043      3378.6714      5833.0910
##   818.554_5.972 818.5539_5.971   721.5493_6    721.549_6 848.5433_6.017
## 2     6304.4487      9040.8240    8293.6070    8293.6070      5426.5117
##   798.5279_6.032 733.5494_6.091 801.5366_6.09 723.5203_6.092 765.5735_6.098
## 2     26310.0040    126621.0100    13265.0940     15401.5330      6608.7260
##   771.5646_6.096 764.4992_6.1 842.5154_6.099 774.5282_6.1 898.559_6.11
## 2     12778.7190    5535.0215      6602.7666   55365.6680    3685.2770
##   763.5596_6.181 1068.6675_6.19 801.547_6.254 790.5149_6.254 800.5438_6.256
## 2     13917.5860     13256.2270    26554.4240      8090.8223     54200.4000
##   868.531_6.255 874.559_6.259 759.5646_6.287 836.5434_6.295 557.4566_6.309
## 2     7316.0400     9442.5750      9749.2750      8236.6190      8280.5880
##   1605.0902_6.358 1604.0878_6.358 882.5021_6.358 824.5441_6.359 814.5148_6.359
## 2       2503.2397       1919.8804      6036.3770    157509.4700     18936.0020
##   892.531_6.359 816.5151_6.359 876.5059_6.36 954.5018_6.361 840.5304_6.407
## 2    16966.9240      7578.4750     9698.0330      5114.8510      8459.3210
##   850.5595_6.407 790.5148_6.407 918.5466_6.408 800.5433_6.41 868.5308_6.41
## 2     65896.5100      6673.6377      8493.9850    57832.4060     6448.3480
##   801.547_6.409 812.5436_6.468 884.518_6.476 879.5263_6.489 816.5318_6.478
## 2    29100.6300     21515.1350     4892.3020      5314.6626     13349.5430
##   818.5302_6.479 956.5168_6.479 894.5465_6.48 1608.119_6.481 826.5598_6.482
## 2      5292.8687      4111.5107    12525.5670      1456.6576    111413.3000
##   878.5241_6.495 1530.1292_6.506 1529.1254_6.507 896.5849_6.505 906.6148_6.513
## 2     15045.6350       7696.4670       7073.6133      5749.3050     25188.6130
##   1519.1193_6.539 1491.1091_6.544 747.5662_6.538 1450.1318_6.54 1441.1057_6.541
## 2      10325.6550       3698.7026   1126503.9000     65371.4060      11406.0780
##   1518.1184_6.541 1502.0921_6.54 739.5351_6.541 1440.1025_6.541 861.5449_6.542
## 2      12439.5910      7361.1310     34892.3630      13591.2950     12531.6820
##   799.5274_6.542 831.526_6.542 764.5548_6.543 737.5362_6.543 815.5524_6.543
## 2     40890.1100     7634.1377     12854.4380     91910.2660     83607.6500
##   877.5224_6.543 1503.101_6.546 788.5437_6.549 805.5234_6.55 883.5394_6.551
## 2     23042.7340      8019.2725     27396.0900    19062.6580     13138.2480
##   945.5095_6.552 1451.1351_6.539 801.5445_6.55 800.5402_6.543 750.528_6.591
## 2      7267.6800      57017.0430    18246.8000     35417.1300    27264.1720
##   820.5694_6.598 784.549_6.62 1151.7045_6.635 838.5592_6.651 876.5748_6.674
## 2      5591.0806     504.0312      16743.6840      3385.6418      5824.5205
##   814.5592_6.699 773.5806_6.749 763.5516_6.749 841.5661_6.75 1579.1408_6.75
## 2      5335.2430    156829.1900     17065.5530    17132.0640      7385.7656
##   1656.1198_6.753 850.5603_6.753 1582.1048_6.758 842.5312_6.754 1657.1231_6.754
## 2       8507.5730    383956.2200       3297.0112     17908.6230       8909.0860
##   853.572_6.759 1646.0906_6.756 840.5306_6.756 902.5217_6.756 867.5493_6.755
## 2    16663.1880       2485.1140     41751.7730     22894.2030      6170.3340
##   986.5339_6.756 918.5467_6.756 964.5391_6.756 970.4999_6.757 908.5177_6.758
## 2      8439.8430     40072.3000      5975.6445      5105.9140     13004.3440
##   980.5167_6.758 776.5438_6.762 1647.0986_6.756 844.5327_6.766 766.5148_6.768
## 2     11173.4750     87543.6100       2520.8220     13258.0540      8478.3030
##   828.5061_6.771 808.5482_6.817 546.1884_6.859 550.1831_6.859 545.3463_6.86
## 2      5257.7920      2870.9343     30991.6600     10394.8545    24574.5300
##   519.347_6.861 548.1856_6.861 557.457_6.865 559.4719_6.867 543.3083_6.873
## 2    18584.6290     31491.5490     3774.5525      4240.5786      8250.8710
##   560.2269_6.891 561.2272_6.889 876.5748_6.891 473.3439_6.896 739.5515_6.896
## 2      5078.9230      5696.8022      8479.1440     10678.1790     11276.6630
##   645.4854_6.897 749.5806_6.906 749.5804_6.907 816.5308_6.915 818.5304_6.915
## 2      6815.6978     23554.2560     23554.2560     92555.0160     35193.7660
##   946.5031_6.923 843.5493_6.926 884.518_6.929 1600.0951_6.94 962.534_6.937
## 2      8240.4930     11779.9900    15864.8000      7945.2354     9319.6450
##   878.522_6.938 1599.0952_6.94 1598.0913_6.94 894.5469_6.945 1676.1068_6.945
## 2    34961.7900      9671.2290      9272.7360     70189.6300       8662.0300
##   956.5171_6.945 940.5392_6.946 1677.1104_6.945 1608.1204_6.95 1609.1238_6.95
## 2     21859.5800     10560.0190       7693.5790     37296.6400     38017.9400
##   826.5606_6.953 886.5195_6.961 1660.08_6.945 810.5642_6.968 1575.0947_6.977
## 2    828296.4400      6658.6170     4503.8657      2061.9120       6851.5527
##   1652.1073_6.976 1574.0913_6.977 1584.1204_6.981 1585.1238_6.981
## 2       7466.5205       8431.1530      34260.9100      33486.1950
##   795.5319_7.041 1561.1246_7.026 802.5612_7.026 1560.1212_7.025 871.5502_7.054
## 2     14637.8060      87123.4700   1357793.5000      96113.1700     41827.1520
##   1551.0949_7.03 1628.1079_7.03 1550.0916_7.031 1629.1111_7.028 917.5422_7.049
## 2     14490.1470     14638.6000      13662.6840      12221.6460      8263.7550
##   1612.0825_7.034 792.5308_7.046 794.5298_7.047 916.5393_7.053 1610.1346_7.034
## 2       7057.5340     86525.6500     35337.5980     12739.0300      22415.3320
##   761.5803_7.053 932.5173_7.054 1611.1384_7.055 854.522_7.057 870.547_7.057
## 2     19171.0760     23813.2150       9165.0530    34324.8630    84686.4800
##   1520.0675_7.067 886.5199_7.016 762.507_7.071 860.5179_7.078 938.534_7.079
## 2       3811.9190      8245.6875    32796.7460     15533.7880    10826.0980
##   922.5024_7.08 1000.5039_7.081 852.5752_7.104 844.5463_7.11 920.5621_7.108
## 2     7704.4320       7322.0195    120860.0800     6777.9770     14412.5760
##   842.546_7.109 904.5364_7.114 1586.1348_7.163 878.5901_7.193 720.4962_7.178
## 2    14882.3710      7048.3574       3374.4092      4193.5596      5568.1133
##   830.5815_7.188 1613.1542_7.188 942.5549_7.193 896.5624_7.191 958.5324_7.191
## 2     39760.6330       4131.9590      6944.3384     34076.4450     10640.0040
##   818.5462_7.192 1612.1504_7.187 880.5375_7.198 828.5759_7.19 820.5463_7.196
## 2     34030.3320       4982.6816     16646.3570   269664.9700     14294.4050
##   886.5328_7.202 948.5172_7.192 964.5495_7.201 881.5397_7.202 1602.121_7.207
## 2     11507.0640      4908.6304      8291.8480      9636.1840      1752.1300
##   788.5223_7.225 834.5643_7.242 881.5332_7.243 807.5001_7.254 790.5593_7.252
## 2      3845.7795     16588.3650      7665.8945      5144.8730     25775.3700
##   864.5747_7.263 738.507_7.269 806.4942_7.271 746.5118_7.267 878.5903_7.305
## 2      7477.0130    39627.3120      5332.4136      3596.2605      7596.1035
##   840.5749_7.321 1602.1228_7.339 948.5173_7.337 886.5331_7.335 897.5657_7.337
## 2     16394.4220       3432.8390      8086.6675     16539.9980     28673.6520
##   1026.5195_7.338 964.5495_7.339 818.5462_7.337 958.5325_7.337 942.5548_7.339
## 2       7702.3306     12165.5910     55049.5550     15019.3560      9148.1640
##   1603.1254_7.34 880.5376_7.34 828.5759_7.339 896.5624_7.34 820.5462_7.34
## 2      4002.3570    25622.8140    430864.8000    51577.6800    22564.8650
##   1612.1508_7.34 845.5636_7.343 1637.1538_7.351 1613.1545_7.34 912.5357_7.346
## 2     10090.1740      8650.3250       3598.2053     10747.8955      5677.1580
##   714.507_7.348 1636.1506_7.352 920.5621_7.373 854.5867_7.421 881.5405_7.337
## 2    21358.9750       3177.6514      9580.4890     18861.1040     12995.1875
##   852.5752_7.369 842.546_7.377 904.5361_7.374 843.5493_7.379 836.5799_7.404
## 2     69645.9300     9225.8790      5982.6323      5791.6274     12651.9100
##   764.5225_7.43 857.5176_7.442 800.5354_7.468 810.5647_7.467 878.5516_7.469
## 2    12744.6630     32854.1400     17461.8550    132522.1700     17583.5500
##   840.5749_7.471 862.527_7.471 868.5303_7.476 862.5261_7.47 800.5357_7.469
## 2     30003.9410    10110.1800      6806.3706    10110.1800     17461.8550
##   922.5767_7.503 854.5896_7.505 740.5225_7.515 833.5175_7.518 855.5937_7.505
## 2      5766.1430     29425.2870      7085.3794     24162.9400     17361.9450
##   862.5953_7.53 884.5607_7.548 776.5354_7.574 816.5751_7.553 806.5463_7.553
## 2     7405.0166      8423.6980     11808.3970     44148.6520      6009.2563
##   833.5544_7.557 884.5587_7.554 843.5839_7.561 767.5664_7.562 905.5552_7.563
## 2     10528.5640      7110.3060     32967.9600     12791.6700     10971.1730
##   765.5672_7.563 775.5965_7.563 786.5646_7.575 854.5519_7.576 776.5351_7.576
## 2     32845.1330    235038.4500     73149.2340      8554.0030     11808.3970
##   778.5595_7.588 768.5305_7.588 830.5218_7.589 846.5477_7.59 836.5799_7.605
## 2    106386.6200     13095.3370      6905.6960    15131.2390     24562.9730
##   883.5333_7.614 746.5121_7.622 814.4993_7.622 812.5802_7.632 864.5398_7.662
## 2      6894.4070     42279.5470      5209.3210     90708.6200      7775.5156
##   802.5529_7.634 880.5669_7.637 1179.7355_7.664 859.5339_7.699 855.5938_7.701
## 2     15428.4590     14540.6350       6533.5780      4189.1255     24039.5940
##   764.5224_7.695 922.5774_7.695 854.5906_7.701 788.5795_7.719 856.5627_7.726
## 2      5594.5650      7022.3516     41821.7930     29939.3220      5997.8154
##   828.5728_7.674 906.5814_7.742 838.5956_7.75 1598.1714_7.758 864.535_7.755
## 2     18499.0640      7983.9310    54082.3300       5964.2130     9644.0780
##   883.5331_7.753 862.5335_7.761 940.5496_7.764 1616.1136_7.768 924.518_7.766
## 2      6612.6390     20426.8340     13661.2270       4574.5190    10243.3730
##   888.5362_7.768 821.565_7.768 1633.1422_7.775 872.5626_7.769 1002.5196_7.769
## 2      7455.2085    11493.6920       8331.0010     87200.1640       6726.3164
##   856.5377_7.769 918.5543_7.768 796.5458_7.771 1555.1258_7.773 934.5326_7.772
## 2     38901.9020     13405.0330     32951.0600       8986.0300     24030.7560
##   794.5464_7.772 1554.1226_7.773 804.5762_7.774 918.5546_7.77 857.5409_7.768
## 2     84274.0600       9048.8280    816784.5600    13405.0330     20997.4380
##   1564.1517_7.775 1632.139_7.776 1565.1551_7.774 772.5276_7.782 1639.1701_7.792
## 2      35523.3500      7901.5444      31104.6350     13494.5000       7250.4443
##   1638.1667_7.792 878.5909_7.805 936.5439_7.803 1008.5435_7.813 870.5618_7.814
## 2       6213.1940     86858.2300      5752.7476       4836.8890      5132.1055
##   946.5777_7.814 868.5616_7.814 930.5504_7.815 1482.0881_7.834 801.6117_7.833
## 2     11800.3270     11311.2530      5586.2490       4683.7026     72313.0800
##   869.598_7.834 791.5828_7.838 824.5789_7.841 814.5949_7.87 864.6108_7.878
## 2    10249.8300     12324.8370      3535.8100    13830.4150      3409.7360
##   722.5123_7.861 790.4993_7.863 820.4801_7.866 780.4705_7.867 814.595_7.869
## 2    115125.7100     12781.5880      6431.2144      6948.0825    13830.4150
##   859.5338_7.864 814.5947_7.872 842.5905_7.891 827.6272_7.905 812.58_7.898
## 2      5668.9233     13830.4150      7701.9053     22540.6450   14386.5610
##   789.6114_7.911 792.5765_7.938 698.5121_7.962 777.6118_7.986 1558.1192_8.016
## 2      5187.0140      5050.9270     17849.7710     14208.7510       3159.3452
##   1052.5353_8.011 990.5651_8.013 974.5321_8.013 912.5491_8.014 748.5277_8.014
## 2       8414.3040     12534.1690      7662.0980     20708.5880     46733.6900
##   906.5531_8.016 922.5781_8.016 871.5805_8.017 846.5617_8.018 1655.1569_8.02
## 2     29787.6840     73024.1950     10395.6310     28928.2580      5415.9004
##   984.5483_8.018 1654.1535_8.02 938.5513_8.018 968.5707_8.019 844.562_8.02
## 2     18987.9920      5260.1740      5926.1855     11094.8690   72785.1500
##   1665.186_8.021 854.5918_8.021 1664.1825_8.021 914.5508_8.03 724.5275_8.059
## 2     15773.8000    597630.8000      16215.7400     9704.4000      6238.5977
##   1631.1566_8.057 814.5957_8.055 1630.1533_8.057 1641.1859_8.06 1640.1826_8.061
## 2       4667.3135      6033.9720       3949.3613     11569.0040      13329.3710
##   1616.183_8.109 945.5734_8.118 1685.1732_8.11 830.5921_8.11 1617.1865_8.109
## 2     30387.2130      9049.9750      8840.7930   810380.7500      33352.4530
##   1684.1698_8.112 1607.1572_8.113 1606.1538_8.113 880.6062_8.116
## 2       8998.0205       9455.1980       8503.0580     24042.2500
##   1668.1442_8.113 944.5705_8.116 822.5616_8.116 820.562_8.117 960.5481_8.117
## 2       5463.8247     13576.0260     37789.9770    85715.2500     24214.5020
##   847.5799_8.118 898.5782_8.119 853.6428_8.12 882.5533_8.121 883.5565_8.118
## 2     12919.2750     89561.4300    24748.5620     37676.0900     19931.1780
##   716.5225_8.139 888.549_8.143 914.5514_8.091 950.5333_8.144 890.5493_8.144
## 2      7779.8290    23152.9470      9903.8900      9753.9375     10276.3010
##   1028.5349_8.145 724.5277_8.141 966.5651_8.147 724.5275_8.142 790.5382_8.16
## 2       8666.2690      7835.7803     18252.3700      7835.7803    21106.9320
##   789.6116_8.177 840.6105_8.214 762.5643_8.206 856.6051_8.21 582.5094_8.227
## 2     11812.9610      4544.3486     18487.7930    13209.3460     10990.9070
##   572.4806_8.229 574.4787_8.23 838.5955_8.279 748.5276_8.299 724.5276_8.3
## 2     21580.7810     7488.7440      8260.9810      8657.7880    4696.7030
##   835.5322_8.336 764.5798_8.343 818.5906_8.346 862.5953_8.355 909.5492_8.36
## 2     16368.2705     15757.4500     17764.0410      6502.1250     8638.5590
##   902.5128_8.383 774.5431_8.358 824.4968_8.383 834.5254_8.385 766.5385_8.385
## 2      4093.0002      2007.0992      8713.0370     16660.0000    102631.6000
##   864.5064_8.385 788.58_8.386 815.627_8.417 736.5275_8.432 1669.2155_8.46
## 2      8448.3670   22432.6460     9359.7300      4345.7607      1621.7545
##   1668.2129_8.463 840.6112_8.445 810.5275_8.448 841.6406_8.452 976.5477_8.457
## 2       2019.3225      9788.1990     33076.2660     11286.6170      3996.8179
##   914.5643_8.457 848.577_8.458 846.5774_8.459 986.5637_8.459 908.5688_8.459
## 2      9376.7980    11834.7740     28534.2230      8202.1890     13272.8750
##   992.5806_8.459 924.5935_8.459 970.5858_8.459 856.6068_8.46 873.5954_8.46
## 2      6013.5340     27763.8200      5290.1294   152973.2700     5111.0654
##   742.5383_8.471 792.5536_8.473 1697.1555_8.491 885.5495_8.509 790.5953_8.511
## 2     33108.3300      5459.1440       2762.2256    219215.0300     23723.5000
##   953.536_8.516 1015.5064_8.516 880.6062_8.517 983.5161_8.518 1021.5212_8.523
## 2    26731.9820       7852.9300     12420.5690     12558.5250       8244.5660
##   943.5073_8.524 1005.4968_8.528 864.6108_8.544 828.5668_8.612 838.5956_8.612
## 2     11931.8100      10613.6810      4830.8320      5983.6357     25094.9750
##   906.5828_8.614 929.5358_8.639 861.5489_8.641 911.5645_8.686 793.5985_8.748
## 2      5728.3066      6024.2460     37498.0820      4402.3364     19151.1910
##   803.6275_8.749 750.5432_8.694 871.6147_8.752 844.6062_8.707 814.5957_8.732
## 2    103716.1600     12871.6200     20451.3440      5184.2810     13738.5530
##   806.5905_8.734 806.5895_8.732 804.6308_8.768 855.59_8.755 861.5846_8.743
## 2     18060.4510     17002.6970     49662.9960   12279.3850     10165.6400
##   795.5979_8.768 793.5985_8.765 842.5306_8.773 774.5433_8.775 803.6275_8.771
## 2      9425.6810     21094.1760      5530.6550     35749.2460    103716.1600
##   871.6147_8.768 933.5855_8.781 840.6113_8.8 830.5838_8.802 908.5977_8.803
## 2     20451.3440      6936.8920   35263.4140      7860.8755      8485.7410
##   700.5277_8.805 819.6141_8.816 829.6431_8.816 821.6152_8.817 856.6029_8.818
## 2      8064.3228     12680.5370     59890.4530      5772.9780      8446.2010
##   887.599_8.816 768.554_8.827 804.6307_8.782 882.6209_8.855 872.5955_8.863
## 2     5530.6300     6555.8037     49662.9960     14926.7080      3023.7730
##   950.6082_8.859 866.6268_8.879 800.5587_8.868 816.6106_8.908 968.581_8.937
## 2      3764.7932      8463.0010      2290.4570      9929.1650     6303.9160
##   946.5851_8.939 824.5771_8.938 962.5631_8.939 890.5649_8.939 900.5936_8.939
## 2      4725.8300     11668.0690      7632.1910     10249.5800     28638.6050
##   832.6067_8.94 884.5687_8.94 822.5774_8.941 897.6305_9.024 829.6432_9.024
## 2   144460.3400    13710.0490     27029.8960     28433.5620    172523.2500
##   881.6055_9.023 819.6141_9.024 959.6018_9.026 1561.2028_9.038 821.6139_9.026
## 2     13610.3370     29945.2170      9545.7750       3594.8525     13116.5080
##   887.5991_9.03 847.6304_9.032 903.5878_9.033 855.6591_9.033 969.6384_9.033
## 2     9205.0660     16887.5350      6131.2227    210398.3100      6098.1714
##   985.616_9.034 991.6331_9.034 907.6211_9.035 923.646_9.035 913.6162_9.035
## 2     9650.0480      8398.3180     20509.0880    40109.9450     13624.2880
##   845.6298_9.037 750.5436_9.042 818.5306_9.045 848.5114_9.046 808.5018_9.051
## 2     40144.5350    111689.3300     14247.4610      7089.5356      6832.3794
##   888.5678_9.068 887.5646_9.068 776.5589_9.132 726.5434_9.157 794.5306_9.158
## 2     14691.8460     23708.0700     13893.1800     22353.0000      4055.4963
##   858.622_9.163 858.6217_9.164 752.5589_9.222 805.6426_9.222 831.6576_9.28
## 2    11971.4860     12272.2260      5368.9220      5129.8857     4040.8533
##   744.5538_9.321 833.6294_9.395 843.6586_9.396 911.6461_9.397 875.6844_9.397
## 2      7145.4883      9015.5790     34686.9000      7792.0415      6487.9380
##   788.5437_9.413 817.643_9.423 885.6305_9.426 807.6141_9.427 887.5646_9.463
## 2     18918.1290    35230.9020      8296.1730      8985.0670      3790.0000
##   600.5119_9.48 752.5591_9.514 857.6737_9.513 931.5514_9.582 863.5644_9.582
## 2     6794.8594      3570.4470      4379.6440      2323.0913     10587.6030
##   911.6459_9.666 901.6169_9.665 895.6211_9.665 833.6297_9.665 843.6587_9.666
## 2     13474.3500      6688.2840      9071.0300     16480.4040     60810.8400
##   911.646_9.667 1016.7241_9.858 983.6184_9.976 778.5744_9.917 889.617_9.974
## 2    13474.3500       4702.8150      5785.7030      7963.8535    13294.1490
##   945.6385_9.975 967.6322_9.976 1670.349_9.987 883.6211_9.976 821.6298_9.976
## 2      5831.8623      8264.8240      4279.3145     18760.4040     40503.6700
##   961.6178_9.977 899.6459_9.978 823.6294_9.979 831.659_9.98 1644.3337_9.984
## 2     11646.7640     41054.9000     16734.7930  224299.7700       6377.4263
##   993.6488_9.986 857.6749_9.986 971.6542_9.988 915.6319_9.987 987.6318_9.987
## 2     10976.1170    269686.7000      7963.2510     17955.4840     12177.7780
##   925.6617_9.988 847.6455_9.988 849.6462_9.988 917.6329_9.989 874.6643_9.99
## 2     48529.5470     49363.0980     23903.6820      7906.3354     7891.7890
##   905.6028_9.99 909.6368_9.99 931.6162_9.992 983.6185_9.993 728.5589_10.045
## 2     8745.6600    23330.1390      5534.3560      5785.7030       5464.5327
##   931.6162_9.999 653.491_10.071 1261.8136_10.084 1235.798_10.088 894.658_10.086
## 2      5534.3560      5309.9985        6648.7560       6083.2900      6683.0166
##   860.6369_10.19 778.5745_10.284 993.6487_10.29 925.6615_10.291 915.6327_10.293
## 2      4347.8887      16747.3610      9180.6270      42885.4570      17120.4940
##   917.633_10.292 857.6746_10.294 849.6458_10.294 967.594_10.294 931.6164_10.288
## 2      8375.1450     227623.8600      29497.3280      8381.0640       5103.2144
##   909.6366_10.295 847.6455_10.298 905.6039_10.298 874.6642_10.298
## 2      31145.9860      68079.2300      10383.8000      12174.1120
##   920.6735_10.31 871.6891_10.337 939.6769_10.335 923.6524_10.353
## 2     11407.9310      23431.1900       4883.7890       4365.2290
##   1261.8135_10.352 754.5745_10.357 833.6742_10.379 860.6919_10.384
## 2        6244.3438       5013.8345      22248.7150      13928.9730
##   823.6447_10.383 818.6271_10.414 828.6559_10.414 820.627_10.414
## 2      10651.2940      49472.2970      36236.8240     24093.9380
##   854.6714_10.418 1646.3475_10.439 904.6327_10.443 913.6613_10.438
## 2      23159.9770        3366.3650      10757.5170      28688.9120
##   1637.3241_10.442 845.6746_10.439 1636.3208_10.44 903.6327_10.442
## 2        4831.9707     248822.1400       6311.0030      18021.2300
##   897.637_10.442 905.6301_10.443 837.6454_10.443 835.6457_10.444 862.664_10.445
## 2     38940.5000       8713.7110      52024.6130     129008.8500     25511.1270
##   955.5948_10.447 920.6223_10.449 893.604_10.45 895.6025_10.452 816.5752_10.467
## 2       9650.6110       7492.4640    18636.5270      13528.3820       5412.6943
##   1249.8137_10.468 638.5721_10.489 628.5437_10.491 630.5417_10.49
## 2        8322.1470      11466.0350      21496.6580      7993.3060
##   1279.8241_10.52 1018.7397_10.524 1008.7111_10.525 654.5589_10.54
## 2       8390.5910        5776.7700        7643.0054     26564.1640
##   656.5586_10.54 664.5877_10.541 682.5753_10.545 680.5744_10.545 707.593_10.547
## 2     10790.8170      15001.5730      15388.2230      33780.4060      8583.8650
##   690.603_10.546 936.6574_10.551 832.6428_10.551 842.6715_10.551
## 2     16967.4600       4246.1646      43308.2800      19995.1740
##   946.6887_10.548 837.6571_10.552 864.6677_10.558 1263.8295_10.575
## 2       3736.5776       5951.1045      12317.8480       17800.0550
##   885.7055_10.58 862.6535_10.583 872.6815_10.583 911.652_10.584 1675.384_10.589
## 2      5392.2827       8905.0270       6678.5770     34630.8320       6900.4870
##   969.6092_10.587 1674.3803_10.59 1666.3553_10.589 859.6907_10.59
## 2       6806.5720       5760.7236        9989.0205    372254.8400
##   849.6616_10.589 1665.3553_10.589 927.677_10.59 876.68_10.59 851.6615_10.59
## 2     232448.1600       12303.3290    27190.5470   45172.9530     98275.5900
##   917.6482_10.589 1664.3521_10.59 919.6483_10.59 907.6204_10.599 909.6189_10.6
## 2      18074.4650      10063.3940      8632.1950      22921.6370    15571.6400
##   934.6384_10.6 912.6573_10.608 652.5879_10.611 642.5594_10.614 806.6057_10.613
## 2     9350.6690      26177.1800       7656.0300       9989.4400       4466.7417
##   922.6891_10.613 896.6737_10.623 914.6592_10.621 970.6153_10.616
## 2      13885.9320       7693.4927       8838.5730       5592.2676
##   886.645_10.627 856.6871_10.641 874.6819_10.641 846.6588_10.643
## 2      6484.7970      41352.6370       9696.1890     105766.6250
##   908.6381_10.644 695.5929_10.644 678.6034_10.645 904.6171_10.646
## 2      11053.4410       6063.0550      12335.3310       8930.5090
##   670.5743_10.646 668.5747_10.647 853.619_10.659 865.6758_10.678
## 2       7433.1140      18218.1840      7036.0166       7932.8877
##   863.6765_10.683 851.6659_10.671 658.5736_10.694 656.575_10.694
## 2      17536.2340      16954.3140      50116.9900    136334.9000
##   666.6032_10.695 744.5811_10.699 684.591_10.697 682.5904_10.698
## 2      80462.1950       5573.4940     47335.4700     121164.5300
##   692.6191_10.698 709.609_10.707 640.5796_10.729 676.6243_10.731
## 2      66447.8000     23455.6050       2462.6406       3118.6467
##   696.6064_10.749 706.6345_10.754 658.5898_10.758 660.5885_10.757
## 2      11036.6760       7231.1826      23411.7600       8107.9395
##   668.6174_10.756 855.6346_10.759 698.6108_10.776 697.6094_10.776
## 2      15633.9460       8517.0140      12544.2450      27215.1540
##   670.5906_10.779 680.6191_10.777 672.5896_10.777 674.6039_10.834
## 2     125584.6700      62872.6900      44991.6500       5083.6850
##   682.6332_10.835 1335.245_10.854 694.635_10.854 684.6065_10.853
## 2       9162.7830       4942.8203    245587.0300     354283.5000
##   686.6054_10.854 746.5966_10.853 1334.2417_10.855 762.6216_10.854
## 2     148661.9800      11560.1420        5303.9520       4838.7534
##   711.6251_10.854 652.6239_10.859 678.6402_10.885 668.611_10.888
## 2      61655.1900       3495.9722       5296.9190      3014.2336
##   698.6217_10.912 700.6211_10.914 708.6502_10.915 686.6207_10.914
## 2      34974.1330      15591.5400      20487.3600      31390.2580
##   697.653_10.915 688.6203_10.915 696.6498_10.915 725.6412_10.916
## 2     11392.3330      12834.5340      21661.5210       8151.1580
##   722.6659_11.002 680.6552_11.016 960.7412_11.038 986.7566_11.049
## 2       5985.1500       3538.8257       9043.6290      11070.3710
##   988.7722_11.162 1014.7875_11.17
## 2      11237.9790       6091.9590

# change to results to numeric
data_notame <- data_notame %>%
  mutate_at(-1, as.numeric)

head(data_notame, n = 1)

##                        mz_rt 272.9587_0.485 288.9363_0.485 226.9658_0.499
## 2 x5101_b1_control_c18neg_82       11986.55        21534.9       28760.87
##   294.9532_0.5 362.9406_0.501 520.9099_0.514 588.8973_0.514 452.9225_0.515
## 2     22338.56       34403.13       114667.8       98069.39       102553.1
##   656.8848_0.515 724.8722_0.515 792.8596_0.515 384.9351_0.516 316.9477_0.518
## 2          59992       46757.65       28809.44       97462.69       129814.3
##   604.8712_0.518 248.9604_0.52 215.0328_0.588 217.0297_0.589 151.0261_0.612
## 2       43247.65      139909.2       423488.8       147959.5       1008.062
##   335.0471_0.599 649.1192_0.601 167.021_0.602 665.0885_0.607 643.1067_0.611
## 2       89597.61       17832.48      169550.4       17469.22       21769.13
##   627.1374_0.611 329.0355_0.613 621.1249_0.613 459.1113_0.613 935.1964_0.613
## 2       35917.11       44170.43       19929.61        12582.8       6787.081
##   919.2277_0.616 605.1555_0.616 313.0652_0.617 291.0839_0.618 128.0352_0.617
## 2       9664.962       76629.05       232746.5        1622795       35370.36
##   583.1735_0.618 247.0933_0.62 293.0879_0.621 191.0197_0.621 111.0086_0.62
## 2       16841.32      30654.05       35340.32       643951.4      150325.2
##   950.1575_0.623 636.085_0.627 658.0669_0.628 680.0486_0.63 389.052_0.635
## 2       3818.781      17712.38       17415.88      6436.066      23057.95
##   263.1035_0.638 277.0734_0.639 343.9948_0.639 172.9913_0.642 187.0418_0.642
## 2        66218.5       41315.56       216509.7       115296.6       209861.1
##   187.0515_0.642 345.9989_0.642 300.0047_0.643 201.0575_0.644 188.9862_0.644
## 2       14098.11           6601       23712.52       48690.59       9552.884
##   89.0243_0.645 230.9967_0.644 93.0344_0.646 178.0509_0.647 479.0972_0.649
## 2      239368.4       1436.521      31256.66       23041.92       31571.95
##   103.04_0.651 462.1763_0.655 187.007_0.66 203.0013_0.66 107.0502_0.661
## 2     48823.08       3000.788     600991.2      7830.959       110424.4
##   194.0457_0.66 255.0871_0.663 383.1529_0.667 187.0165_0.665 117.0557_0.669
## 2      3220.508       8777.292       1042.219       16411.95       56197.16
##   291.0953_0.67 245.0486_0.685 352.0856_0.687 201.0226_0.688 413.1998_0.688
## 2      15675.74       17020.85       8563.765       1560.347       6060.373
##   437.0542_0.688 510.2523_0.688 624.3379_0.689 514.2836_0.69 213.0223_0.689
## 2        1315.98       6418.269       6967.838      504.0312        3920.36
##   397.1781_0.69 129.0557_0.69 367.1581_0.691 257.0795_0.669 528.2631_0.691
## 2      13469.26      126977.6        36765.1       504.0312         7024.1
##   231.0795_0.662 397.2049_0.693 229.0537_0.703 244.908_0.706 246.9051_0.707
## 2       1033.337       10682.89       1605.157      28453.58       26781.31
##   369.1735_0.722 137.0244_0.741 624.3382_0.91 239.0923_0.975 464.3013_0.999
## 2       16647.08       2228.712      2404.195       13168.31       504.0312
##   241.0868_1.048 448.3063_1.326 453.285_1.371 583.2555_1.358 448.3062_1.477
## 2       504.0312       2249.928      504.0312       8213.148       2152.334
##   267.1235_1.539 507.223_1.7 437.2904_2.11 586.3143_2.221 391.2849_2.246
## 2       14312.37    28249.83      2109.412       7916.017       7834.511
##   562.3143_2.27 507.223_2.273 297.1527_2.281 512.2988_2.28 311.2224_2.465
## 2      6131.514      5463.416       4852.787      18104.64       4982.177
##   538.3144_2.488 449.254_2.573 612.33_2.594 640.2923_2.673 656.3173_2.673
## 2       26836.06      3670.256     27236.77       8631.628       11211.98
##   578.3011_2.673 588.3302_2.677 526.3144_2.683 311.1683_2.682 1107.6619_2.7
## 2       11934.06       156921.7       14725.99       10671.61      6479.082
##   517.2436_2.68 524.2776_2.71 830.5005_2.723 1151.649_2.727 504.3089_2.726
## 2      504.0312      17717.52       1502.127       2805.045       18348.54
##   564.3308_2.727 1135.6242_2.73 556.299_2.728 694.2877_2.728 1083.6621_2.728
## 2         396450        2101.62      10870.73       11570.62        15289.02
##   616.2924_2.728 554.3012_2.728 1073.6331_2.728 632.3174_2.729 1602.9935_2.726
## 2       20911.83       28204.33        3184.225       25367.39        1053.242
##   619.2886_2.757 500.2777_2.802 614.3455_2.824 476.2777_2.847 590.3457_2.989
## 2       12485.94       35047.24       7444.078       27902.56       39904.79
##   1521.9681_3.075 670.2878_3.073 1165.6197_3.074 532.2989_3.075 530.3013_3.075
## 2        2808.471       21550.03        6353.824       23154.22       62886.28
##   1025.6334_3.075 592.2925_3.075 540.3313_3.076 608.3174_3.076 1035.6627_3.076
## 2        12987.27       42669.53        1157628        52637.3        93593.68
##   654.31_3.076 1103.6493_3.076 480.309_3.076 1087.6246_3.076 1531.9972_3.076
## 2      8150.14        14690.84      47245.85        10563.33        7595.639
##   1520.9647_3.076 1027.6336_3.076 1530.994_3.076 1598.9806_3.076
## 2         3523.74        6002.621       10113.03        2481.021
##   1315.8467_3.079 820.5155_3.079 1061.6776_3.089 452.2777_3.197 618.308_3.232
## 2        504.0312       2857.127         7589.48       23911.54      14584.35
##   634.3329_3.232 506.3245_3.232 566.3462_3.232 556.3168_3.233 696.3033_3.233
## 2        20147.4       10277.21       273539.3       25799.98       7031.487
##   1087.6933_3.233 558.3148_3.234 478.2933_3.354 524.3351_3.376 526.3505_3.4
## 2        7558.609       8811.278       16215.15       21914.58     18850.23
##   592.3612_3.407 554.3456_3.441 436.2827_3.522 552.3663_3.532 698.3189_3.775
## 2       7309.226       20630.93       13906.46       11245.39       8278.129
##   1159.7117_3.776 1081.6957_3.777 508.3402_3.777 568.3621_3.777 620.3237_3.777
## 2         4318.52        5479.515       16299.13       532299.4       24095.65
##   1143.6869_3.778 636.3486_3.777 558.3325_3.778 1091.7248_3.778 626.3198_3.778
## 2        3467.048       39472.61       42671.38        21538.79          10507
##   560.3303_3.778 1615.0859_3.778 594.3768_3.858 480.309_3.883 599.3194_3.921
## 2       14168.75        2119.961       6830.728      45650.44        15658.1
##   583.2554_3.939 605.2373_3.94 1167.518_3.94 554.3818_4.037 414.0583_4.043
## 2       23206.53      6507.553      5111.501       7755.415       8064.445
##   253.2169_4.044 327.2325_4.055 511.3996_4.087 464.314_4.127 303.2326_4.149
## 2       3715.852       40238.29       3120.451      17825.38       43368.23
##   440.0739_4.207 442.071_4.208 279.2327_4.208 444.0682_4.208 559.4722_4.208
## 2        56946.7      46998.21         129523       17429.46        8758.29
##   463.3422_4.219 537.4153_4.24 539.4304_4.266 539.4306_4.266 573.4515_4.266
## 2        4484.45      33903.05       13237.36       13237.36       11812.46
##   445.3316_4.28 467.3735_4.306 591.462_4.314 445.3316_4.415 539.4309_4.416
## 2      16675.65        24954.8      5188.547       22877.84       10927.55
##   511.3997_4.453 447.3471_4.499 493.389_4.514 447.3471_4.631 473.3629_4.632
## 2       3652.242       11653.17       5953.37        11625.7        5888.98
##   567.4621_4.643 446.0838_4.646 444.0866_4.647 281.2484_4.648 442.0896_4.647
## 2       4465.416       19138.68       50135.16       200246.8       51583.44
##   563.5036_4.648 591.462_4.651 449.3627_4.663 593.4778_4.664 575.467_4.662
## 2       23532.04      6583.166       9165.393       35374.88      3355.543
##   473.3474_4.696 425.363_4.712 441.3942_4.718 491.3733_4.784 818.5544_4.825
## 2       504.0312      12562.76       4651.295       4547.798       504.0312
##   551.3582_4.841 573.4515_4.873 561.3787_4.908 577.3738_4.918 617.4753_4.956
## 2       1659.143       5616.502        8191.66       5274.489       3398.378
##   595.4933_4.953 691.5022_4.977 495.4046_4.993 397.3681_5 717.5179_5.106
## 2       23644.17       11188.64        4821.53   6125.191       27211.07
##   465.3192_5.112 465.3038_5.114 591.3894_5.174 705.5178_5.308 479.3193_5.338
## 2       8263.396       110951.5        11246.6       6977.324       6625.671
##   575.4672_5.354 411.3837_5.349 605.4051_5.422 579.3893_5.557 878.584_5.681
## 2       3909.946       7838.022       6459.481       2674.295       4946.87
##   1394.0685_5.684 787.5209_5.683 709.5047_5.684 719.534_5.684 771.4961_5.685
## 2        4091.168       22534.19       23499.24      222421.6       10974.93
##   711.5035_5.685 849.4911_5.685 493.3349_5.699 722.4967_5.728 904.599_5.836
## 2       9168.952       6182.067       10544.26       3234.964      6464.149
##   1447.1028_5.846 1446.0998_5.845 875.5066_5.845 735.5203_5.846 813.5365_5.846
## 2        8393.435           10199        9896.56       37915.44       33982.08
##   737.5193_5.846 745.5498_5.846 859.5292_5.846 803.5076_5.847 797.5117_5.847
## 2       15724.55       381718.7        6394.41       11439.64       18180.04
##   1271.7465_5.862 848.5434_5.871 822.5279_5.875 748.5122_5.882 824.5433_5.925
## 2        9264.096       5360.774       6334.904       3378.671       5833.091
##   818.554_5.972 818.5539_5.971 721.5493_6 721.549_6 848.5433_6.017
## 2      6304.449       9040.824   8293.607  8293.607       5426.512
##   798.5279_6.032 733.5494_6.091 801.5366_6.09 723.5203_6.092 765.5735_6.098
## 2          26310         126621      13265.09       15401.53       6608.726
##   771.5646_6.096 764.4992_6.1 842.5154_6.099 774.5282_6.1 898.559_6.11
## 2       12778.72     5535.021       6602.767     55365.67     3685.277
##   763.5596_6.181 1068.6675_6.19 801.547_6.254 790.5149_6.254 800.5438_6.256
## 2       13917.59       13256.23      26554.42       8090.822        54200.4
##   868.531_6.255 874.559_6.259 759.5646_6.287 836.5434_6.295 557.4566_6.309
## 2       7316.04      9442.575       9749.275       8236.619       8280.588
##   1605.0902_6.358 1604.0878_6.358 882.5021_6.358 824.5441_6.359 814.5148_6.359
## 2         2503.24         1919.88       6036.377       157509.5          18936
##   892.531_6.359 816.5151_6.359 876.5059_6.36 954.5018_6.361 840.5304_6.407
## 2      16966.92       7578.475      9698.033       5114.851       8459.321
##   850.5595_6.407 790.5148_6.407 918.5466_6.408 800.5433_6.41 868.5308_6.41
## 2       65896.51       6673.638       8493.985      57832.41      6448.348
##   801.547_6.409 812.5436_6.468 884.518_6.476 879.5263_6.489 816.5318_6.478
## 2      29100.63       21515.13      4892.302       5314.663       13349.54
##   818.5302_6.479 956.5168_6.479 894.5465_6.48 1608.119_6.481 826.5598_6.482
## 2       5292.869       4111.511      12525.57       1456.658       111413.3
##   878.5241_6.495 1530.1292_6.506 1529.1254_6.507 896.5849_6.505 906.6148_6.513
## 2       15045.64        7696.467        7073.613       5749.305       25188.61
##   1519.1193_6.539 1491.1091_6.544 747.5662_6.538 1450.1318_6.54 1441.1057_6.541
## 2        10325.66        3698.703        1126504       65371.41        11406.08
##   1518.1184_6.541 1502.0921_6.54 739.5351_6.541 1440.1025_6.541 861.5449_6.542
## 2        12439.59       7361.131       34892.36         13591.3       12531.68
##   799.5274_6.542 831.526_6.542 764.5548_6.543 737.5362_6.543 815.5524_6.543
## 2       40890.11      7634.138       12854.44       91910.27       83607.65
##   877.5224_6.543 1503.101_6.546 788.5437_6.549 805.5234_6.55 883.5394_6.551
## 2       23042.73       8019.273       27396.09      19062.66       13138.25
##   945.5095_6.552 1451.1351_6.539 801.5445_6.55 800.5402_6.543 750.528_6.591
## 2        7267.68        57017.04       18246.8       35417.13      27264.17
##   820.5694_6.598 784.549_6.62 1151.7045_6.635 838.5592_6.651 876.5748_6.674
## 2       5591.081     504.0312        16743.68       3385.642        5824.52
##   814.5592_6.699 773.5806_6.749 763.5516_6.749 841.5661_6.75 1579.1408_6.75
## 2       5335.243       156829.2       17065.55      17132.06       7385.766
##   1656.1198_6.753 850.5603_6.753 1582.1048_6.758 842.5312_6.754 1657.1231_6.754
## 2        8507.573       383956.2        3297.011       17908.62        8909.086
##   853.572_6.759 1646.0906_6.756 840.5306_6.756 902.5217_6.756 867.5493_6.755
## 2      16663.19        2485.114       41751.77        22894.2       6170.334
##   986.5339_6.756 918.5467_6.756 964.5391_6.756 970.4999_6.757 908.5177_6.758
## 2       8439.843        40072.3       5975.645       5105.914       13004.34
##   980.5167_6.758 776.5438_6.762 1647.0986_6.756 844.5327_6.766 766.5148_6.768
## 2       11173.48       87543.61        2520.822       13258.05       8478.303
##   828.5061_6.771 808.5482_6.817 546.1884_6.859 550.1831_6.859 545.3463_6.86
## 2       5257.792       2870.934       30991.66       10394.85      24574.53
##   519.347_6.861 548.1856_6.861 557.457_6.865 559.4719_6.867 543.3083_6.873
## 2      18584.63       31491.55      3774.552       4240.579       8250.871
##   560.2269_6.891 561.2272_6.889 876.5748_6.891 473.3439_6.896 739.5515_6.896
## 2       5078.923       5696.802       8479.144       10678.18       11276.66
##   645.4854_6.897 749.5806_6.906 749.5804_6.907 816.5308_6.915 818.5304_6.915
## 2       6815.698       23554.26       23554.26       92555.02       35193.77
##   946.5031_6.923 843.5493_6.926 884.518_6.929 1600.0951_6.94 962.534_6.937
## 2       8240.493       11779.99       15864.8       7945.235      9319.645
##   878.522_6.938 1599.0952_6.94 1598.0913_6.94 894.5469_6.945 1676.1068_6.945
## 2      34961.79       9671.229       9272.736       70189.63         8662.03
##   956.5171_6.945 940.5392_6.946 1677.1104_6.945 1608.1204_6.95 1609.1238_6.95
## 2       21859.58       10560.02        7693.579       37296.64       38017.94
##   826.5606_6.953 886.5195_6.961 1660.08_6.945 810.5642_6.968 1575.0947_6.977
## 2       828296.4       6658.617      4503.866       2061.912        6851.553
##   1652.1073_6.976 1574.0913_6.977 1584.1204_6.981 1585.1238_6.981
## 2         7466.52        8431.153        34260.91        33486.19
##   795.5319_7.041 1561.1246_7.026 802.5612_7.026 1560.1212_7.025 871.5502_7.054
## 2       14637.81        87123.47        1357794        96113.17       41827.15
##   1551.0949_7.03 1628.1079_7.03 1550.0916_7.031 1629.1111_7.028 917.5422_7.049
## 2       14490.15        14638.6        13662.68        12221.65       8263.755
##   1612.0825_7.034 792.5308_7.046 794.5298_7.047 916.5393_7.053 1610.1346_7.034
## 2        7057.534       86525.65        35337.6       12739.03        22415.33
##   761.5803_7.053 932.5173_7.054 1611.1384_7.055 854.522_7.057 870.547_7.057
## 2       19171.08       23813.22        9165.053      34324.86      84686.48
##   1520.0675_7.067 886.5199_7.016 762.507_7.071 860.5179_7.078 938.534_7.079
## 2        3811.919       8245.688      32796.75       15533.79       10826.1
##   922.5024_7.08 1000.5039_7.081 852.5752_7.104 844.5463_7.11 920.5621_7.108
## 2      7704.432         7322.02       120860.1      6777.977       14412.58
##   842.546_7.109 904.5364_7.114 1586.1348_7.163 878.5901_7.193 720.4962_7.178
## 2      14882.37       7048.357        3374.409        4193.56       5568.113
##   830.5815_7.188 1613.1542_7.188 942.5549_7.193 896.5624_7.191 958.5324_7.191
## 2       39760.63        4131.959       6944.338       34076.44          10640
##   818.5462_7.192 1612.1504_7.187 880.5375_7.198 828.5759_7.19 820.5463_7.196
## 2       34030.33        4982.682       16646.36        269665       14294.41
##   886.5328_7.202 948.5172_7.192 964.5495_7.201 881.5397_7.202 1602.121_7.207
## 2       11507.06        4908.63       8291.848       9636.184        1752.13
##   788.5223_7.225 834.5643_7.242 881.5332_7.243 807.5001_7.254 790.5593_7.252
## 2        3845.78       16588.37       7665.895       5144.873       25775.37
##   864.5747_7.263 738.507_7.269 806.4942_7.271 746.5118_7.267 878.5903_7.305
## 2       7477.013      39627.31       5332.414        3596.26       7596.104
##   840.5749_7.321 1602.1228_7.339 948.5173_7.337 886.5331_7.335 897.5657_7.337
## 2       16394.42        3432.839       8086.667          16540       28673.65
##   1026.5195_7.338 964.5495_7.339 818.5462_7.337 958.5325_7.337 942.5548_7.339
## 2        7702.331       12165.59       55049.56       15019.36       9148.164
##   1603.1254_7.34 880.5376_7.34 828.5759_7.339 896.5624_7.34 820.5462_7.34
## 2       4002.357      25622.81       430864.8      51577.68      22564.87
##   1612.1508_7.34 845.5636_7.343 1637.1538_7.351 1613.1545_7.34 912.5357_7.346
## 2       10090.17       8650.325        3598.205        10747.9       5677.158
##   714.507_7.348 1636.1506_7.352 920.5621_7.373 854.5867_7.421 881.5405_7.337
## 2      21358.97        3177.651       9580.489        18861.1       12995.19
##   852.5752_7.369 842.546_7.377 904.5361_7.374 843.5493_7.379 836.5799_7.404
## 2       69645.93      9225.879       5982.632       5791.627       12651.91
##   764.5225_7.43 857.5176_7.442 800.5354_7.468 810.5647_7.467 878.5516_7.469
## 2      12744.66       32854.14       17461.85       132522.2       17583.55
##   840.5749_7.471 862.527_7.471 868.5303_7.476 862.5261_7.47 800.5357_7.469
## 2       30003.94      10110.18       6806.371      10110.18       17461.85
##   922.5767_7.503 854.5896_7.505 740.5225_7.515 833.5175_7.518 855.5937_7.505
## 2       5766.143       29425.29       7085.379       24162.94       17361.94
##   862.5953_7.53 884.5607_7.548 776.5354_7.574 816.5751_7.553 806.5463_7.553
## 2      7405.017       8423.698        11808.4       44148.65       6009.256
##   833.5544_7.557 884.5587_7.554 843.5839_7.561 767.5664_7.562 905.5552_7.563
## 2       10528.56       7110.306       32967.96       12791.67       10971.17
##   765.5672_7.563 775.5965_7.563 786.5646_7.575 854.5519_7.576 776.5351_7.576
## 2       32845.13       235038.5       73149.23       8554.003        11808.4
##   778.5595_7.588 768.5305_7.588 830.5218_7.589 846.5477_7.59 836.5799_7.605
## 2       106386.6       13095.34       6905.696      15131.24       24562.97
##   883.5333_7.614 746.5121_7.622 814.4993_7.622 812.5802_7.632 864.5398_7.662
## 2       6894.407       42279.55       5209.321       90708.62       7775.516
##   802.5529_7.634 880.5669_7.637 1179.7355_7.664 859.5339_7.699 855.5938_7.701
## 2       15428.46       14540.64        6533.578       4189.126       24039.59
##   764.5224_7.695 922.5774_7.695 854.5906_7.701 788.5795_7.719 856.5627_7.726
## 2       5594.565       7022.352       41821.79       29939.32       5997.815
##   828.5728_7.674 906.5814_7.742 838.5956_7.75 1598.1714_7.758 864.535_7.755
## 2       18499.06       7983.931      54082.33        5964.213      9644.078
##   883.5331_7.753 862.5335_7.761 940.5496_7.764 1616.1136_7.768 924.518_7.766
## 2       6612.639       20426.83       13661.23        4574.519      10243.37
##   888.5362_7.768 821.565_7.768 1633.1422_7.775 872.5626_7.769 1002.5196_7.769
## 2       7455.208      11493.69        8331.001       87200.16        6726.316
##   856.5377_7.769 918.5543_7.768 796.5458_7.771 1555.1258_7.773 934.5326_7.772
## 2        38901.9       13405.03       32951.06         8986.03       24030.76
##   794.5464_7.772 1554.1226_7.773 804.5762_7.774 918.5546_7.77 857.5409_7.768
## 2       84274.06        9048.828       816784.6      13405.03       20997.44
##   1564.1517_7.775 1632.139_7.776 1565.1551_7.774 772.5276_7.782 1639.1701_7.792
## 2        35523.35       7901.544        31104.63        13494.5        7250.444
##   1638.1667_7.792 878.5909_7.805 936.5439_7.803 1008.5435_7.813 870.5618_7.814
## 2        6213.194       86858.23       5752.748        4836.889       5132.105
##   946.5777_7.814 868.5616_7.814 930.5504_7.815 1482.0881_7.834 801.6117_7.833
## 2       11800.33       11311.25       5586.249        4683.703       72313.08
##   869.598_7.834 791.5828_7.838 824.5789_7.841 814.5949_7.87 864.6108_7.878
## 2      10249.83       12324.84        3535.81      13830.42       3409.736
##   722.5123_7.861 790.4993_7.863 820.4801_7.866 780.4705_7.867 814.595_7.869
## 2       115125.7       12781.59       6431.214       6948.083      13830.42
##   859.5338_7.864 814.5947_7.872 842.5905_7.891 827.6272_7.905 812.58_7.898
## 2       5668.923       13830.42       7701.905       22540.65     14386.56
##   789.6114_7.911 792.5765_7.938 698.5121_7.962 777.6118_7.986 1558.1192_8.016
## 2       5187.014       5050.927       17849.77       14208.75        3159.345
##   1052.5353_8.011 990.5651_8.013 974.5321_8.013 912.5491_8.014 748.5277_8.014
## 2        8414.304       12534.17       7662.098       20708.59       46733.69
##   906.5531_8.016 922.5781_8.016 871.5805_8.017 846.5617_8.018 1655.1569_8.02
## 2       29787.68        73024.2       10395.63       28928.26         5415.9
##   984.5483_8.018 1654.1535_8.02 938.5513_8.018 968.5707_8.019 844.562_8.02
## 2       18987.99       5260.174       5926.185       11094.87     72785.15
##   1665.186_8.021 854.5918_8.021 1664.1825_8.021 914.5508_8.03 724.5275_8.059
## 2        15773.8       597630.8        16215.74        9704.4       6238.598
##   1631.1566_8.057 814.5957_8.055 1630.1533_8.057 1641.1859_8.06 1640.1826_8.061
## 2        4667.314       6033.972        3949.361          11569        13329.37
##   1616.183_8.109 945.5734_8.118 1685.1732_8.11 830.5921_8.11 1617.1865_8.109
## 2       30387.21       9049.975       8840.793      810380.8        33352.45
##   1684.1698_8.112 1607.1572_8.113 1606.1538_8.113 880.6062_8.116
## 2        8998.021        9455.198        8503.058       24042.25
##   1668.1442_8.113 944.5705_8.116 822.5616_8.116 820.562_8.117 960.5481_8.117
## 2        5463.825       13576.03       37789.98      85715.25        24214.5
##   847.5799_8.118 898.5782_8.119 853.6428_8.12 882.5533_8.121 883.5565_8.118
## 2       12919.27       89561.43      24748.56       37676.09       19931.18
##   716.5225_8.139 888.549_8.143 914.5514_8.091 950.5333_8.144 890.5493_8.144
## 2       7779.829      23152.95        9903.89       9753.938        10276.3
##   1028.5349_8.145 724.5277_8.141 966.5651_8.147 724.5275_8.142 790.5382_8.16
## 2        8666.269        7835.78       18252.37        7835.78      21106.93
##   789.6116_8.177 840.6105_8.214 762.5643_8.206 856.6051_8.21 582.5094_8.227
## 2       11812.96       4544.349       18487.79      13209.35       10990.91
##   572.4806_8.229 574.4787_8.23 838.5955_8.279 748.5276_8.299 724.5276_8.3
## 2       21580.78      7488.744       8260.981       8657.788     4696.703
##   835.5322_8.336 764.5798_8.343 818.5906_8.346 862.5953_8.355 909.5492_8.36
## 2       16368.27       15757.45       17764.04       6502.125      8638.559
##   902.5128_8.383 774.5431_8.358 824.4968_8.383 834.5254_8.385 766.5385_8.385
## 2           4093       2007.099       8713.037          16660       102631.6
##   864.5064_8.385 788.58_8.386 815.627_8.417 736.5275_8.432 1669.2155_8.46
## 2       8448.367     22432.65       9359.73       4345.761       1621.755
##   1668.2129_8.463 840.6112_8.445 810.5275_8.448 841.6406_8.452 976.5477_8.457
## 2        2019.322       9788.199       33076.27       11286.62       3996.818
##   914.5643_8.457 848.577_8.458 846.5774_8.459 986.5637_8.459 908.5688_8.459
## 2       9376.798      11834.77       28534.22       8202.189       13272.88
##   992.5806_8.459 924.5935_8.459 970.5858_8.459 856.6068_8.46 873.5954_8.46
## 2       6013.534       27763.82       5290.129      152973.3      5111.065
##   742.5383_8.471 792.5536_8.473 1697.1555_8.491 885.5495_8.509 790.5953_8.511
## 2       33108.33       5459.144        2762.226         219215        23723.5
##   953.536_8.516 1015.5064_8.516 880.6062_8.517 983.5161_8.518 1021.5212_8.523
## 2      26731.98         7852.93       12420.57       12558.52        8244.566
##   943.5073_8.524 1005.4968_8.528 864.6108_8.544 828.5668_8.612 838.5956_8.612
## 2       11931.81        10613.68       4830.832       5983.636       25094.97
##   906.5828_8.614 929.5358_8.639 861.5489_8.641 911.5645_8.686 793.5985_8.748
## 2       5728.307       6024.246       37498.08       4402.336       19151.19
##   803.6275_8.749 750.5432_8.694 871.6147_8.752 844.6062_8.707 814.5957_8.732
## 2       103716.2       12871.62       20451.34       5184.281       13738.55
##   806.5905_8.734 806.5895_8.732 804.6308_8.768 855.59_8.755 861.5846_8.743
## 2       18060.45        17002.7          49663     12279.39       10165.64
##   795.5979_8.768 793.5985_8.765 842.5306_8.773 774.5433_8.775 803.6275_8.771
## 2       9425.681       21094.18       5530.655       35749.25       103716.2
##   871.6147_8.768 933.5855_8.781 840.6113_8.8 830.5838_8.802 908.5977_8.803
## 2       20451.34       6936.892     35263.41       7860.876       8485.741
##   700.5277_8.805 819.6141_8.816 829.6431_8.816 821.6152_8.817 856.6029_8.818
## 2       8064.323       12680.54       59890.45       5772.978       8446.201
##   887.599_8.816 768.554_8.827 804.6307_8.782 882.6209_8.855 872.5955_8.863
## 2       5530.63      6555.804          49663       14926.71       3023.773
##   950.6082_8.859 866.6268_8.879 800.5587_8.868 816.6106_8.908 968.581_8.937
## 2       3764.793       8463.001       2290.457       9929.165      6303.916
##   946.5851_8.939 824.5771_8.938 962.5631_8.939 890.5649_8.939 900.5936_8.939
## 2        4725.83       11668.07       7632.191       10249.58        28638.6
##   832.6067_8.94 884.5687_8.94 822.5774_8.941 897.6305_9.024 829.6432_9.024
## 2      144460.3      13710.05        27029.9       28433.56       172523.2
##   881.6055_9.023 819.6141_9.024 959.6018_9.026 1561.2028_9.038 821.6139_9.026
## 2       13610.34       29945.22       9545.775        3594.852       13116.51
##   887.5991_9.03 847.6304_9.032 903.5878_9.033 855.6591_9.033 969.6384_9.033
## 2      9205.066       16887.53       6131.223       210398.3       6098.171
##   985.616_9.034 991.6331_9.034 907.6211_9.035 923.646_9.035 913.6162_9.035
## 2      9650.048       8398.318       20509.09      40109.94       13624.29
##   845.6298_9.037 750.5436_9.042 818.5306_9.045 848.5114_9.046 808.5018_9.051
## 2       40144.54       111689.3       14247.46       7089.536       6832.379
##   888.5678_9.068 887.5646_9.068 776.5589_9.132 726.5434_9.157 794.5306_9.158
## 2       14691.85       23708.07       13893.18          22353       4055.496
##   858.622_9.163 858.6217_9.164 752.5589_9.222 805.6426_9.222 831.6576_9.28
## 2      11971.49       12272.23       5368.922       5129.886      4040.853
##   744.5538_9.321 833.6294_9.395 843.6586_9.396 911.6461_9.397 875.6844_9.397
## 2       7145.488       9015.579        34686.9       7792.042       6487.938
##   788.5437_9.413 817.643_9.423 885.6305_9.426 807.6141_9.427 887.5646_9.463
## 2       18918.13       35230.9       8296.173       8985.067           3790
##   600.5119_9.48 752.5591_9.514 857.6737_9.513 931.5514_9.582 863.5644_9.582
## 2      6794.859       3570.447       4379.644       2323.091        10587.6
##   911.6459_9.666 901.6169_9.665 895.6211_9.665 833.6297_9.665 843.6587_9.666
## 2       13474.35       6688.284        9071.03        16480.4       60810.84
##   911.646_9.667 1016.7241_9.858 983.6184_9.976 778.5744_9.917 889.617_9.974
## 2      13474.35        4702.815       5785.703       7963.854      13294.15
##   945.6385_9.975 967.6322_9.976 1670.349_9.987 883.6211_9.976 821.6298_9.976
## 2       5831.862       8264.824       4279.315        18760.4       40503.67
##   961.6178_9.977 899.6459_9.978 823.6294_9.979 831.659_9.98 1644.3337_9.984
## 2       11646.76        41054.9       16734.79     224299.8        6377.426
##   993.6488_9.986 857.6749_9.986 971.6542_9.988 915.6319_9.987 987.6318_9.987
## 2       10976.12       269686.7       7963.251       17955.48       12177.78
##   925.6617_9.988 847.6455_9.988 849.6462_9.988 917.6329_9.989 874.6643_9.99
## 2       48529.55        49363.1       23903.68       7906.335      7891.789
##   905.6028_9.99 909.6368_9.99 931.6162_9.992 983.6185_9.993 728.5589_10.045
## 2       8745.66      23330.14       5534.356       5785.703        5464.533
##   931.6162_9.999 653.491_10.071 1261.8136_10.084 1235.798_10.088 894.658_10.086
## 2       5534.356       5309.998         6648.756         6083.29       6683.017
##   860.6369_10.19 778.5745_10.284 993.6487_10.29 925.6615_10.291 915.6327_10.293
## 2       4347.889        16747.36       9180.627        42885.46        17120.49
##   917.633_10.292 857.6746_10.294 849.6458_10.294 967.594_10.294 931.6164_10.288
## 2       8375.145        227623.9        29497.33       8381.064        5103.214
##   909.6366_10.295 847.6455_10.298 905.6039_10.298 874.6642_10.298
## 2        31145.99        68079.23         10383.8        12174.11
##   920.6735_10.31 871.6891_10.337 939.6769_10.335 923.6524_10.353
## 2       11407.93        23431.19        4883.789        4365.229
##   1261.8135_10.352 754.5745_10.357 833.6742_10.379 860.6919_10.384
## 2         6244.344        5013.834        22248.72        13928.97
##   823.6447_10.383 818.6271_10.414 828.6559_10.414 820.627_10.414
## 2        10651.29         49472.3        36236.82       24093.94
##   854.6714_10.418 1646.3475_10.439 904.6327_10.443 913.6613_10.438
## 2        23159.98         3366.365        10757.52        28688.91
##   1637.3241_10.442 845.6746_10.439 1636.3208_10.44 903.6327_10.442
## 2         4831.971        248822.1        6311.003        18021.23
##   897.637_10.442 905.6301_10.443 837.6454_10.443 835.6457_10.444 862.664_10.445
## 2        38940.5        8713.711        52024.61        129008.9       25511.13
##   955.5948_10.447 920.6223_10.449 893.604_10.45 895.6025_10.452 816.5752_10.467
## 2        9650.611        7492.464      18636.53        13528.38        5412.694
##   1249.8137_10.468 638.5721_10.489 628.5437_10.491 630.5417_10.49
## 2         8322.147        11466.03        21496.66       7993.306
##   1279.8241_10.52 1018.7397_10.524 1008.7111_10.525 654.5589_10.54
## 2        8390.591          5776.77         7643.005       26564.16
##   656.5586_10.54 664.5877_10.541 682.5753_10.545 680.5744_10.545 707.593_10.547
## 2       10790.82        15001.57        15388.22        33780.41       8583.865
##   690.603_10.546 936.6574_10.551 832.6428_10.551 842.6715_10.551
## 2       16967.46        4246.165        43308.28        19995.17
##   946.6887_10.548 837.6571_10.552 864.6677_10.558 1263.8295_10.575
## 2        3736.578        5951.105        12317.85         17800.06
##   885.7055_10.58 862.6535_10.583 872.6815_10.583 911.652_10.584 1675.384_10.589
## 2       5392.283        8905.027        6678.577       34630.83        6900.487
##   969.6092_10.587 1674.3803_10.59 1666.3553_10.589 859.6907_10.59
## 2        6806.572        5760.724         9989.021       372254.8
##   849.6616_10.589 1665.3553_10.589 927.677_10.59 876.68_10.59 851.6615_10.59
## 2        232448.2         12303.33      27190.55     45172.95       98275.59
##   917.6482_10.589 1664.3521_10.59 919.6483_10.59 907.6204_10.599 909.6189_10.6
## 2        18074.47        10063.39       8632.195        22921.64      15571.64
##   934.6384_10.6 912.6573_10.608 652.5879_10.611 642.5594_10.614 806.6057_10.613
## 2      9350.669        26177.18         7656.03         9989.44        4466.742
##   922.6891_10.613 896.6737_10.623 914.6592_10.621 970.6153_10.616
## 2        13885.93        7693.493        8838.573        5592.268
##   886.645_10.627 856.6871_10.641 874.6819_10.641 846.6588_10.643
## 2       6484.797        41352.64        9696.189        105766.6
##   908.6381_10.644 695.5929_10.644 678.6034_10.645 904.6171_10.646
## 2        11053.44        6063.055        12335.33        8930.509
##   670.5743_10.646 668.5747_10.647 853.619_10.659 865.6758_10.678
## 2        7433.114        18218.18       7036.017        7932.888
##   863.6765_10.683 851.6659_10.671 658.5736_10.694 656.575_10.694
## 2        17536.23        16954.31        50116.99       136334.9
##   666.6032_10.695 744.5811_10.699 684.591_10.697 682.5904_10.698
## 2         80462.2        5573.494       47335.47        121164.5
##   692.6191_10.698 709.609_10.707 640.5796_10.729 676.6243_10.731
## 2         66447.8        23455.6        2462.641        3118.647
##   696.6064_10.749 706.6345_10.754 658.5898_10.758 660.5885_10.757
## 2        11036.68        7231.183        23411.76         8107.94
##   668.6174_10.756 855.6346_10.759 698.6108_10.776 697.6094_10.776
## 2        15633.95        8517.014        12544.25        27215.15
##   670.5906_10.779 680.6191_10.777 672.5896_10.777 674.6039_10.834
## 2        125584.7        62872.69        44991.65        5083.685
##   682.6332_10.835 1335.245_10.854 694.635_10.854 684.6065_10.853
## 2        9162.783         4942.82         245587        354283.5
##   686.6054_10.854 746.5966_10.853 1334.2417_10.855 762.6216_10.854
## 2          148662        11560.14         5303.952        4838.753
##   711.6251_10.854 652.6239_10.859 678.6402_10.885 668.611_10.888
## 2        61655.19        3495.972        5296.919       3014.234
##   698.6217_10.912 700.6211_10.914 708.6502_10.915 686.6207_10.914
## 2        34974.13        15591.54        20487.36        31390.26
##   697.653_10.915 688.6203_10.915 696.6498_10.915 725.6412_10.916
## 2       11392.33        12834.53        21661.52        8151.158
##   722.6659_11.002 680.6552_11.016 960.7412_11.038 986.7566_11.049
## 2         5985.15        3538.826        9043.629        11070.37
##   988.7722_11.162 1014.7875_11.17
## 2        11237.98        6091.959

Find connections

connection <- find_connections(data = data_notame,
                               features = features,
                               corr_thresh = 0.95,
                               rt_window = 1/60,
                               name_col = "mz_rt",
                               mz_col = "mz",
                               rt_col = "rt")

## [1] 100
## [1] 200
## [1] 300
## [1] 400
## [1] 500
## [1] 600
## [1] 700
## [1] 800
## [1] 900
## [1] 1000

head(connection)

##                x              y       cor rt_diff   mz_diff
## 1 191.0197_0.621  111.0086_0.62 0.9588214  -0.001  -80.0111
## 2 187.0418_0.642  89.0243_0.645 0.9844393   0.003  -98.0175
## 3 479.0972_0.649   187.007_0.66 0.9591729   0.011 -292.0902
## 4 479.0972_0.649 107.0502_0.661 0.9559836   0.012 -372.0470
## 5   187.007_0.66 107.0502_0.661 0.9879347   0.001  -79.9568
## 6  244.908_0.706 246.9051_0.707 0.9675337   0.001    1.9971

Clustering

clusters <- find_clusters(connections = connection, d_thresh = 0.8)

## 58 components found
## 
## 14 components found
## 
## 1 components found

# assign a cluster ID to all features. Clusters are named after feature with highest median peak height
features_clustered <- assign_cluster_id(data_notame, clusters, features, name_col = "mz_rt")

# lets see how many features are removed when we only keep one feature per cluster
pulled <- pull_clusters(data_notame, features_clustered, name_col = "mz_rt")
cluster_data <- pulled$cdata
cluster_features <- pulled$cfeatures

# how much did we trim our data down by?
nrow(omicsdata) - nrow(cluster_features)

## [1] 81

# export clustered feature list
write_csv(cluster_features,
          "Notame/notame-clustered-features.csv")

Reduce dataset based on clustering

# transpose the full dataset back to wide so that it is more similar to the notame dataset
imp_meta_omics_wide <- imp_meta_omics %>%
  dplyr::select(-"row_id") %>%
  pivot_wider(names_from = mz_rt,
              values_from = peak_height)

# list of reduced features
clusternames <- cluster_features$mz_rt

# select only the features are in the reduced list
imp_clust <- imp_meta_omics_wide[,c(names(imp_meta_omics_wide) %in% clusternames)]

# bind back sample names
imp_clust <- cbind(imp_meta_omics_wide[1], imp_clust)

tibble(imp_clust)

## # A tibble: 85 × 955
##    sample      `272.9587_0.485` `288.9363_0.485` `226.9658_0.499` `294.9532_0.5`
##    <chr>                  <dbl>            <dbl>            <dbl>          <dbl>
##  1 x5101_b1_c…           11987.           21535.           28761.         22339.
##  2 x5105_b3_l…           15548.           23409.           32647.         24825.
##  3 x5112_b1_b…           14778.           19901.           31099.         23495.
##  4 x5107_b1_b…           13083.           21959.           31327.         24307.
##  5 x5105_b1_l…           14824.           23221.           35289.         23222.
##  6 x5102_b1_c…           15760.           24398.           30828.         22992.
##  7 x5101_b3_c…           13294.           20615.           33028.         25811.
##  8 x5103_b1_l…           13628.           19189.           26919.         22856.
##  9 x5104_b3_l…           12694.           18564.           30674.         25084.
## 10 x5109_b3_b…           12346.           22480.           31363.         23632.
## # ℹ 75 more rows
## # ℹ 950 more variables: `362.9406_0.501` <dbl>, `520.9099_0.514` <dbl>,
## #   `588.8973_0.514` <dbl>, `452.9225_0.515` <dbl>, `656.8848_0.515` <dbl>,
## #   `724.8722_0.515` <dbl>, `792.8596_0.515` <dbl>, `384.9351_0.516` <dbl>,
## #   `316.9477_0.518` <dbl>, `604.8712_0.518` <dbl>, `248.9604_0.52` <dbl>,
## #   `215.0328_0.588` <dbl>, `217.0297_0.589` <dbl>, `151.0261_0.612` <dbl>,
## #   `335.0471_0.599` <dbl>, `649.1192_0.601` <dbl>, `167.021_0.602` <dbl>, …

New mz vs rt plot

Let’s see how our clustered data looks now compared to the original

# plot new rt vs mz scatterplot post-clustering
(plot_mzvsrt_postcluster <- cluster_features %>%
  ggplot(aes(x = rt,
             y = mz)) +
  geom_point() +
  theme_minimal() +
  labs(x = "Retention time, min",
       y = "m/z, neutral",
       title = "mz across RT for all features after clustering"))

# plot both scatterplots to compare with and without notame clustering
(scatterplots <- ggarrange(plot_mzvsrt, 
                           plot_mzvsrt_postcluster, 
                           nrow = 2))

Wrangle new data

# add subject column back for a seamless join with metadata
imp_clust$subject <- imp_meta_omics_wide$subject

# bind back new clustered data with metadata
imp_metabind_clust <- right_join(metadata, 
                                 imp_clust,
                                 by = "subject")

Visualize untransformed data

More wrangling

# meta data columns 
str_meta <- colnames(imp_metabind_clust[,4:13])

# change meta data columns to character so that I can change NAs from QCs to "QC"
imp_metabind_clust <- imp_metabind_clust %>%
  mutate_at(str_meta, as.character) 

# replace NAs in metadata columns for QCs
imp_metabind_clust[is.na(imp_metabind_clust)] <- "QC"

# long df
imp_metabind_clust_tidy <- imp_metabind_clust %>%
  pivot_longer(cols = 15:ncol(.),
               names_to = "mz_rt",
               values_to = "rel_abund")

# structure check
head(imp_metabind_clust_tidy)

## # A tibble: 6 × 16
##   subject treatment tomato_or_control sex   bmi   age   tot_chol ldl_chol
##   <chr>   <chr>     <chr>             <chr> <chr> <chr> <chr>    <chr>   
## 1 5107    beta      tomato            M     23.81 68    177      85.4    
## 2 5107    beta      tomato            M     23.81 68    177      85.4    
## 3 5107    beta      tomato            M     23.81 68    177      85.4    
## 4 5107    beta      tomato            M     23.81 68    177      85.4    
## 5 5107    beta      tomato            M     23.81 68    177      85.4    
## 6 5107    beta      tomato            M     23.81 68    177      85.4    
## # ℹ 8 more variables: hdl_chol <chr>, triglycerides <chr>, glucose <chr>,
## #   SBP <chr>, DBP <chr>, sample <chr>, mz_rt <chr>, rel_abund <dbl>

Boxplot

imp_metabind_clust_tidy %>%
  ggplot(aes(x = subject, y = rel_abund, color = treatment)) +
  geom_boxplot(alpha = 0.6) +
  scale_color_manual(values = c("orange", "lightgreen", "gray", "tomato1")) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(title = "LC-MS (+) Feature Abundances by Sample",
       subtitle = "Unscaled data",
       y = "Relative abundance")

Will need to log transform in order to normalize and actually see the data

Log2 transform

imp_metabind_clust_tidy_log2 <- imp_metabind_clust_tidy %>%
  mutate(rel_abund_log2 = log2(rel_abund))

Boxplot

(bp_data_quality <- imp_metabind_clust_tidy_log2 %>%
  ggplot(aes(x = sample, y = rel_abund_log2, color = treatment)) +
  geom_boxplot(alpha = 0.6) +
  scale_color_manual(values = c("orange", "lightgreen", "gray", "tomato1")) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 90)) +
  labs(title = "LC-MS (+) Feature Abundances by Sample",
       subtitle = "Log2 transformed data",
       y = "Relative abundance"))

Notame drift correction

Data wrangling

Feature abund df

# filtered and imputed data after notame clustering, transposed
features_forQCcorr <- imp_clust %>%
  dplyr::select(!subject) %>%
  t() %>%
  as.data.frame() %>%
  row_to_names(row_number = "find_header")

# log2 transform
log2_features_forQCcorr <- features_forQCcorr %>%
  mutate_all(as.numeric) %>%
  log2()

# write csv to manually edit
write.csv(log2_features_forQCcorr,
          "Notame/feaures_fromR_forDC_1.csv",
          row.names = TRUE)

Import corrected df (edited so that “mz_rt” could be the row name for row 1)

log2_features_forQCcorr_new <- read.csv("Notame/feaures_forR_forDC_editedmzrt_2.csv",
                                header = FALSE,
                                row.names = 1)


log2_features_forQCcorr_new <- log2_features_forQCcorr_new %>%
  rownames_to_column(var = "mz_rt") %>%
  row_to_names(row_number = 1) %>%
  separate(col = mz_rt,
           into = c("mz", "rt"),
           sep = "_")

write.csv(log2_features_forQCcorr_new,
          "Notame/features_fromR_forDC_3.csv",
          row.names = TRUE)

Pheno df

# separate sampleID and injection order
pheno_data <- imp_clust[1] %>%
  separate(col = sample,
           into = c("sample", "injection_order"),
           # last underscore
           sep = "_(?!.*_)")



# make inj order column numeric
pheno_data <- pheno_data %>%
  mutate_at("injection_order", as.numeric)

t_pheno_data <- as.data.frame(t(pheno_data))

write.csv(t_pheno_data,
          "Notame/pheno_df.csv",
          row.names = TRUE)

Combine pheno and feature dfs manually in excel to create metaboset df.

Import Metaboset

#make sure when converting csv to xlsx that you save as a new file, don't just change the name of the file
metaboset <- read_from_excel("Notame/metaboset.xlsx",
                             split_by = c("column", "Ion mode"))

## INFO [2025-09-30 18:38:00] Detecting corner position
## INFO [2025-09-30 18:38:00] Corner detected correctly at row 3, column D
## INFO [2025-09-30 18:38:00] 
## Extracting sample information from rows 1 to 3 and columns E to CK
## INFO [2025-09-30 18:38:00] Replacing spaces in sample information column names with underscores (_)
## INFO [2025-09-30 18:38:00] Naming the last column of sample information "Datafile"
## INFO [2025-09-30 18:38:00] 
## Extracting feature information from rows 4 to 957 and columns A to D
## INFO [2025-09-30 18:38:00] Creating Split column from column, Ion mode
## INFO [2025-09-30 18:38:00] Feature_ID column not found, creating feature IDs
## INFO [2025-09-30 18:38:00] Identified m/z column mass and retention time column rt
## INFO [2025-09-30 18:38:00] Identified m/z column mass and retention time column rt
## INFO [2025-09-30 18:38:00] Creating feature IDs from Split, m/z and retention time
## INFO [2025-09-30 18:38:01] Replacing dots (.) in feature information column names with underscores (_)
## INFO [2025-09-30 18:38:01] 
## Extracting feature abundances from rows 4 to 957 and columns E to CK
## INFO [2025-09-30 18:38:01] 
## Checking sample information
## INFO [2025-09-30 18:38:01] QC column generated from rows containing 'QC'
## INFO [2025-09-30 18:38:01] Sample ID autogenerated from injection orders and prefix ID_
## INFO [2025-09-30 18:38:01] Checking that feature abundances only contain numeric values
## INFO [2025-09-30 18:38:01] 
## Checking feature information
## INFO [2025-09-30 18:38:01] Checking that feature IDs are unique and not stored as numbers
## INFO [2025-09-30 18:38:01] Checking that m/z and retention time values are reasonable
## INFO [2025-09-30 18:38:01] Identified m/z column mass and retention time column rt
## INFO [2025-09-30 18:38:01] Identified m/z column mass and retention time column rt

#construct Metaboset
modes <- construct_metabosets(exprs = metaboset$exprs,
                              pheno_data = metaboset$pheno_data,
                              feature_data = metaboset$feature_data, group_col = "Class")

## Initializing the object(s) with unflagged features
## INFO [2025-09-30 18:38:01] 
## Checking feature information
## INFO [2025-09-30 18:38:01] Checking that feature IDs are unique and not stored as numbers
## INFO [2025-09-30 18:38:01] Checking that feature abundances only contain numeric values
## INFO [2025-09-30 18:38:01] Setting row and column names of exprs based on feature and pheno data

#extract each mode into a single object
mode <- modes$C18_neg

Boxplots before correction

# ordered by injection
(qualityBPs_b4correction <- plot_sample_boxplots(mode, order_by = c("Class", "Injection_order"), title = "Uncorrected feature abundance"))

#ordered by class
plot_sample_boxplots(mode, order_by = "Injection_order", title = "Uncorrected feature abundance")

Boxplots after QC drift correction

drift correction takes up to 2 minutes

mode <- flag_detection(mode, qc_limit = 0.75, group_limit = 0.8)

## INFO [2025-09-30 18:38:05] 
## 0% of features flagged for low detection rate

corrected <- correct_drift(mode, log_transform = FALSE)

## INFO [2025-09-30 18:38:05] 
## Starting drift correction at 2025-09-30 18:38:05.815966
## INFO [2025-09-30 18:38:09] Drift correction performed at 2025-09-30 18:38:09.396701
## INFO [2025-09-30 18:38:11] Inspecting drift correction results 2025-09-30 18:38:11.026608
## INFO [2025-09-30 18:38:15] Drift correction results inspected at 2025-09-30 18:38:15.678307
## INFO [2025-09-30 18:38:15] 
## Drift correction results inspected, report:
## Drift_corrected: 100%

Did drift correction work?

output is percent of the features that were drift corrected. The remaining “low-quality” percent represents features for which the DC did not improve the RSD and D-ratio of the original data.

inspected <- inspect_dc(orig = mode, dc = corrected, check_quality = TRUE)

## INFO [2025-09-30 18:38:20] Inspecting drift correction results 2025-09-30 18:38:20.057847
## INFO [2025-09-30 18:38:26] Drift correction results inspected at 2025-09-30 18:38:26.226664
## INFO [2025-09-30 18:38:26] 
## Drift correction results inspected, report:
## Drift_corrected: 59%,  Low_quality: 41%

Boxplots, corrected

(qualityBPS_driftcorrection <- plot_sample_boxplots(corrected, order_by = c("Class", "Injection_order"), title = "Corrected feature abundance"))

plot_sample_boxplots(corrected, order_by = "Injection_order", title = "Corrected feature abundance")

Compare quality BPs

(qualityBPs_compared <- ggarrange(qualityBPs_b4correction, qualityBPS_driftcorrection,
                    ncol = 1, nrow = 2))

Export new Metaboset to Excel spreadsheet

write_to_excel(corrected, "Notame/metaboset_corrected.xlsx")

Manually edit the df so it only has mass, rt, and sample columns

Import edited Metaboset

metabdata_corrected <- read.csv(file = "Notame/metaboset_corrected_editedforR.csv",
                                check.names = FALSE)

Wrangle new metab data

Combine mz & rt back together

metabdata_corrected_MZ_RT <- metabdata_corrected %>%
  mutate(mass = round(metabdata_corrected$mass, digits = 4), # Decrease number of decimals for m/z & rt
         rt = round(metabdata_corrected$rt, digits = 3),
         .before=1,
         .keep="unused") %>%
  unite(mz_rt, c(mass, rt), remove=TRUE) # Combine m/z & rt with _ in between

Transpose new df

metabdata_corrected_t <- as.data.frame(t(metabdata_corrected_MZ_RT)) %>%
  row_to_names(row_number = "find_header") %>% # make MZ_RT column names
  rownames_to_column(var = "sample") # change rownames to column 1

Bind new data with metadata

I want the new “metabdata_corrected_t” df to look just like “imp_metabind_clust_log2” df

# go back to wide data
imp_metabind_clust_log2 <- imp_metabind_clust_tidy_log2 %>%
  dplyr::select(!rel_abund) %>%
  pivot_wider(names_from = mz_rt,
              values_from = rel_abund_log2)

# bind metadata columns to the new drift corrected df
DC_imp_metabind_clust_log2 <- full_join(imp_metabind_clust_log2[,c(1:14)], 
                                        metabdata_corrected_t, 
                                        by = "sample")

# fix QC subject names
DC_imp_metabind_clust_log2$subject <- str_replace_all(DC_imp_metabind_clust_log2$subject, "c", "qc")

DC_imp_metabind_clust_log2 <- DC_imp_metabind_clust_log2 %>%
  mutate_at("subject", str_sub, start=1, end=4)


# make feature abundances numeric
DC_imp_metabind_clust_log2 <- DC_imp_metabind_clust_log2 %>%
  mutate_at(15:ncol(.), as.numeric)

PCAs

With QCS

Wrangle

PCA.DC_imp_metabind_clust_log2 <- PCA(DC_imp_metabind_clust_log2,  # wide data
                                   quali.sup = 1:14, # remove qualitative variables
                                   graph = FALSE, # don't graph
                                   scale.unit = FALSE) # don't scale, already transformed data

# PCA summary
kable(summary(PCA.DC_imp_metabind_clust_log2))

## 
## Call:
## PCA(X = DC_imp_metabind_clust_log2, scale.unit = FALSE, quali.sup = 1:14,  
##      graph = FALSE) 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6   Dim.7
## Variance              19.842  16.531  13.524  12.302  11.065   8.404   7.496
## % of var.             11.452   9.541   7.805   7.100   6.386   4.850   4.326
## Cumulative % of var.  11.452  20.993  28.798  35.898  42.284  47.134  51.460
##                        Dim.8   Dim.9  Dim.10  Dim.11  Dim.12  Dim.13  Dim.14
## Variance               6.892   6.245   5.624   4.849   4.459   3.990   3.607
## % of var.              3.978   3.604   3.246   2.799   2.573   2.303   2.082
## Cumulative % of var.  55.438  59.042  62.288  65.087  67.660  69.963  72.045
##                       Dim.15  Dim.16  Dim.17  Dim.18  Dim.19  Dim.20  Dim.21
## Variance               3.115   3.029   2.830   2.559   2.397   2.150   2.001
## % of var.              1.798   1.748   1.633   1.477   1.384   1.241   1.155
## Cumulative % of var.  73.842  75.590  77.224  78.701  80.084  81.325  82.480
##                       Dim.22  Dim.23  Dim.24  Dim.25  Dim.26  Dim.27  Dim.28
## Variance               1.931   1.852   1.567   1.413   1.359   1.247   1.189
## % of var.              1.114   1.069   0.905   0.816   0.784   0.720   0.686
## Cumulative % of var.  83.594  84.663  85.568  86.383  87.167  87.887  88.574
##                       Dim.29  Dim.30  Dim.31  Dim.32  Dim.33  Dim.34  Dim.35
## Variance               1.135   1.103   0.953   0.946   0.853   0.824   0.790
## % of var.              0.655   0.636   0.550   0.546   0.492   0.476   0.456
## Cumulative % of var.  89.229  89.865  90.416  90.962  91.454  91.930  92.385
##                       Dim.36  Dim.37  Dim.38  Dim.39  Dim.40  Dim.41  Dim.42
## Variance               0.727   0.723   0.637   0.618   0.575   0.541   0.499
## % of var.              0.420   0.417   0.368   0.357   0.332   0.312   0.288
## Cumulative % of var.  92.805  93.222  93.590  93.947  94.278  94.590  94.878
##                       Dim.43  Dim.44  Dim.45  Dim.46  Dim.47  Dim.48  Dim.49
## Variance               0.476   0.446   0.428   0.423   0.389   0.382   0.342
## % of var.              0.275   0.258   0.247   0.244   0.225   0.220   0.197
## Cumulative % of var.  95.153  95.411  95.658  95.902  96.127  96.347  96.545
##                       Dim.50  Dim.51  Dim.52  Dim.53  Dim.54  Dim.55  Dim.56
## Variance               0.336   0.322   0.309   0.294   0.277   0.272   0.263
## % of var.              0.194   0.186   0.179   0.170   0.160   0.157   0.152
## Cumulative % of var.  96.739  96.925  97.103  97.273  97.433  97.590  97.742
##                       Dim.57  Dim.58  Dim.59  Dim.60  Dim.61  Dim.62  Dim.63
## Variance               0.249   0.240   0.231   0.219   0.204   0.196   0.192
## % of var.              0.144   0.139   0.133   0.127   0.118   0.113   0.111
## Cumulative % of var.  97.886  98.024  98.157  98.284  98.402  98.515  98.625
##                       Dim.64  Dim.65  Dim.66  Dim.67  Dim.68  Dim.69  Dim.70
## Variance               0.185   0.170   0.162   0.157   0.148   0.147   0.140
## % of var.              0.107   0.098   0.093   0.090   0.085   0.085   0.081
## Cumulative % of var.  98.732  98.830  98.924  99.014  99.099  99.184  99.265
##                       Dim.71  Dim.72  Dim.73  Dim.74  Dim.75  Dim.76  Dim.77
## Variance               0.137   0.126   0.125   0.119   0.109   0.106   0.100
## % of var.              0.079   0.072   0.072   0.069   0.063   0.061   0.057
## Cumulative % of var.  99.344  99.417  99.489  99.558  99.621  99.682  99.739
##                       Dim.78  Dim.79  Dim.80  Dim.81  Dim.82  Dim.83  Dim.84
## Variance               0.092   0.088   0.082   0.080   0.074   0.036   0.000
## % of var.              0.053   0.051   0.047   0.046   0.043   0.021   0.000
## Cumulative % of var.  99.792  99.843  99.890  99.936  99.979 100.000 100.000
## 
## Individuals (the 10 first)
##                       Dist     Dim.1     ctr    cos2     Dim.2     ctr    cos2
## 1                |  20.533 |  -1.184   0.083   0.003 |  -2.038   0.296   0.010
## 2                |  16.185 |   3.466   0.712   0.046 |   3.498   0.871   0.047
## 3                |  14.348 |  -5.882   2.051   0.168 |   0.788   0.044   0.003
## 4                |  18.070 | -12.004   8.544   0.441 |  -4.188   1.248   0.054
## 5                |  13.723 |  -5.894   2.059   0.184 |   3.357   0.802   0.060
## 6                |  12.703 |  -1.170   0.081   0.008 |   5.334   2.025   0.176
## 7                |  13.491 |  -4.288   1.090   0.101 |  -5.111   1.859   0.144
## 8                |  21.865 | -12.565   9.361   0.330 | -11.661   9.678   0.284
## 9                |  16.181 |  -1.640   0.159   0.010 |   6.056   2.610   0.140
## 10               |  15.124 |   0.660   0.026   0.002 |   8.082   4.648   0.286
##                      Dim.3     ctr    cos2  
## 1                | -11.016  10.557   0.288 |
## 2                |  -4.630   1.865   0.082 |
## 3                |  -1.115   0.108   0.006 |
## 4                |  -3.346   0.974   0.034 |
## 5                |  -2.145   0.400   0.024 |
## 6                |  -1.038   0.094   0.007 |
## 7                |   3.687   1.183   0.075 |
## 8                |   3.611   1.135   0.027 |
## 9                |  -5.668   2.795   0.123 |
## 10               |  -0.871   0.066   0.003 |
## 
## Variables (the 10 first)
##                     Dim.1    ctr   cos2    Dim.2    ctr   cos2    Dim.3    ctr
## 272.9587_0.485   | -0.003  0.000  0.000 | -0.025  0.004  0.020 |  0.011  0.001
## 288.9363_0.485   |  0.017  0.001  0.013 | -0.035  0.007  0.055 |  0.023  0.004
## 226.9658_0.499   |  0.015  0.001  0.021 | -0.010  0.001  0.010 |  0.001  0.000
## 294.9532_0.5     |  0.016  0.001  0.019 |  0.006  0.000  0.003 |  0.006  0.000
## 362.9406_0.501   |  0.015  0.001  0.027 | -0.005  0.000  0.004 | -0.001  0.000
## 520.9099_0.514   |  0.003  0.000  0.002 |  0.018  0.002  0.059 | -0.016  0.002
## 588.8973_0.514   | -0.005  0.000  0.005 |  0.025  0.004  0.110 | -0.009  0.001
## 452.9225_0.515   | -0.001  0.000  0.000 |  0.015  0.001  0.064 | -0.011  0.001
## 656.8848_0.515   |  0.012  0.001  0.014 |  0.029  0.005  0.082 | -0.022  0.004
## 724.8722_0.515   | -0.003  0.000  0.001 |  0.026  0.004  0.069 | -0.016  0.002
##                    cos2  
## 272.9587_0.485    0.004 |
## 288.9363_0.485    0.024 |
## 226.9658_0.499    0.000 |
## 294.9532_0.5      0.003 |
## 362.9406_0.501    0.000 |
## 520.9099_0.514    0.044 |
## 588.8973_0.514    0.014 |
## 452.9225_0.515    0.036 |
## 656.8848_0.515    0.048 |
## 724.8722_0.515    0.028 |
## 
## Supplementary categories (the 10 first)
##                      Dist    Dim.1   cos2 v.test    Dim.2   cos2 v.test  
## 5101             | 10.444 | -1.428  0.019 -0.456 |  2.925  0.078  1.024 |
## 5102             | 10.618 | -2.638  0.062 -0.842 | -1.669  0.025 -0.584 |
## 5103             | 12.500 | -5.469  0.191 -1.747 | -2.939  0.055 -1.028 |
## 5104             | 11.850 | -6.483  0.299 -2.071 | -5.359  0.205 -1.875 |
## 5105             | 13.414 | -4.523  0.114 -1.445 | -5.311  0.157 -1.858 |
## 5107             | 16.936 |  1.141  0.005  0.364 |  0.730  0.002  0.255 |
## 5108             | 14.400 | -3.240  0.051 -1.035 | -0.927  0.004 -0.324 |
## 5109             | 14.516 | -8.943  0.380 -2.856 | -1.700  0.014 -0.595 |
## 5110             | 12.013 | -4.309  0.129 -1.376 |  2.852  0.056  0.998 |
## 5111             | 12.776 | -1.482  0.013 -0.473 | -2.394  0.035 -0.838 |
##                   Dim.3   cos2 v.test  
## 5101              0.027  0.000  0.010 |
## 5102              4.581  0.186  1.772 |
## 5103              0.366  0.001  0.142 |
## 5104              1.728  0.021  0.669 |
## 5105             -0.270  0.000 -0.104 |
## 5107             -7.823  0.213 -3.026 |
## 5108             -6.267  0.189 -2.425 |
## 5109             -2.230  0.024 -0.863 |
## 5110             -3.463  0.083 -1.340 |
## 5111              7.980  0.390  3.087 |

		Dist		Dim.1	cos2	v.test		Dim.2	cos2	v.test		Dim.3	cos2	v.test
5101	|	10.444	|	-1.428	0.019	-0.456	|	2.925	0.078	1.024	|	0.027	0.000	0.010	|
5102	|	10.618	|	-2.638	0.062	-0.842	|	-1.669	0.025	-0.584	|	4.581	0.186	1.772	|
5103	|	12.500	|	-5.469	0.191	-1.747	|	-2.939	0.055	-1.028	|	0.366	0.001	0.142	|
5104	|	11.850	|	-6.483	0.299	-2.071	|	-5.359	0.205	-1.875	|	1.728	0.021	0.669	|
5105	|	13.414	|	-4.523	0.114	-1.445	|	-5.311	0.157	-1.858	|	-0.270	0.000	-0.104	|
5107	|	16.936	|	1.141	0.005	0.364	|	0.730	0.002	0.255	|	-7.823	0.213	-3.026	|
5108	|	14.400	|	-3.240	0.051	-1.035	|	-0.927	0.004	-0.324	|	-6.267	0.189	-2.425	|
5109	|	14.516	|	-8.943	0.380	-2.856	|	-1.700	0.014	-0.595	|	-2.230	0.024	-0.863	|
5110	|	12.013	|	-4.309	0.129	-1.376	|	2.852	0.056	0.998	|	-3.463	0.083	-1.340	|
5111	|	12.776	|	-1.482	0.013	-0.473	|	-2.394	0.035	-0.838	|	7.980	0.390	3.087	|

# pull PC coordinates into df
PC_coord_QC_log2 <- as.data.frame(PCA.DC_imp_metabind_clust_log2$ind$coord)

# bind back metadata from cols 1-14
PC_coord_QC_log2 <- bind_cols(DC_imp_metabind_clust_log2[,1:14], PC_coord_QC_log2)

# grab some variance explained
importance_QC <- PCA.DC_imp_metabind_clust_log2$eig

# set variance explained with PC1, round to 2 digits
PC1_withQC <- round(importance_QC[1,2], 2)

# set variance explained with PC2, round to 2 digits
PC2_withQC <- round(importance_QC[2,2], 2)

Plots

Using FactoExtra package

# scree plot
fviz_eig(PCA.DC_imp_metabind_clust_log2)

# get eigenvalues
kable(get_eig(PCA.DC_imp_metabind_clust_log2))

	eigenvalue	variance.percent	cumulative.variance.percent
Dim.1	19.8421755	11.4518536	11.45185
Dim.2	16.5307904	9.5406974	20.99255
Dim.3	13.5237358	7.8051846	28.79774
Dim.4	12.3020654	7.1001011	35.89784
Dim.5	11.0647299	6.3859765	42.28381
Dim.6	8.4040513	4.8503736	47.13419
Dim.7	7.4956565	4.3260962	51.46028
Dim.8	6.8924751	3.9779718	55.43825
Dim.9	6.2447866	3.6041603	59.04242
Dim.10	5.6237895	3.2457537	62.28817
Dim.11	4.8493397	2.7987822	65.08695
Dim.12	4.4587015	2.5733265	67.66028
Dim.13	3.9898243	2.3027154	69.96299
Dim.14	3.6072051	2.0818879	72.04488
Dim.15	3.1145811	1.7975714	73.84245
Dim.16	3.0286837	1.7479960	75.59045
Dim.17	2.8301684	1.6334234	77.22387
Dim.18	2.5587894	1.4767978	78.70067
Dim.19	2.3971540	1.3835104	80.08418
Dim.20	2.1496833	1.2406834	81.32486
Dim.21	2.0008810	1.1548026	82.47967
Dim.22	1.9309956	1.1144685	83.59413
Dim.23	1.8520226	1.0688894	84.66302
Dim.24	1.5674275	0.9046362	85.56766
Dim.25	1.4130659	0.8155469	86.38321
Dim.26	1.3585949	0.7841091	87.16732
Dim.27	1.2472250	0.7198322	87.88715
Dim.28	1.1894317	0.6864770	88.57363
Dim.29	1.1353607	0.6552701	89.22890
Dim.30	1.1028196	0.6364891	89.86538
Dim.31	0.9532155	0.5501455	90.41553
Dim.32	0.9463791	0.5461999	90.96173
Dim.33	0.8528924	0.4922444	91.45397
Dim.34	0.8239744	0.4755544	91.92953
Dim.35	0.7900000	0.4559462	92.38547
Dim.36	0.7269618	0.4195639	92.80504
Dim.37	0.7230376	0.4172990	93.22234
Dim.38	0.6367755	0.3675131	93.58985
Dim.39	0.6181784	0.3567799	93.94663
Dim.40	0.5747396	0.3317093	94.27834
Dim.41	0.5406162	0.3120151	94.59036
Dim.42	0.4989367	0.2879599	94.87832
Dim.43	0.4764383	0.2749750	95.15329
Dim.44	0.4463250	0.2575952	95.41089
Dim.45	0.4281641	0.2471136	95.65800
Dim.46	0.4232934	0.2443026	95.90230
Dim.47	0.3890995	0.2245677	96.12687
Dim.48	0.3818560	0.2203871	96.34726
Dim.49	0.3420712	0.1974254	96.54468
Dim.50	0.3359414	0.1938876	96.73857
Dim.51	0.3224576	0.1861055	96.92467
Dim.52	0.3094896	0.1786210	97.10330
Dim.53	0.2938807	0.1696124	97.27291
Dim.54	0.2771909	0.1599799	97.43289
Dim.55	0.2718670	0.1569073	97.58980
Dim.56	0.2630650	0.1518272	97.74162
Dim.57	0.2493399	0.1439058	97.88553
Dim.58	0.2401879	0.1386238	98.02415
Dim.59	0.2310412	0.1333448	98.15750
Dim.60	0.2193334	0.1265876	98.28408
Dim.61	0.2038445	0.1176482	98.40173
Dim.62	0.1959995	0.1131205	98.51485
Dim.63	0.1915519	0.1105536	98.62541
Dim.64	0.1851354	0.1068503	98.73226
Dim.65	0.1699516	0.0980870	98.83034
Dim.66	0.1619971	0.0934962	98.92384
Dim.67	0.1565721	0.0903652	99.01421
Dim.68	0.1475668	0.0851678	99.09937
Dim.69	0.1468370	0.0847466	99.18412
Dim.70	0.1401048	0.0808611	99.26498
Dim.71	0.1372662	0.0792228	99.34420
Dim.72	0.1255668	0.0724705	99.41667
Dim.73	0.1251464	0.0722279	99.48890
Dim.74	0.1192392	0.0688186	99.55772
Dim.75	0.1089387	0.0628736	99.62059
Dim.76	0.1062115	0.0612997	99.68189
Dim.77	0.0996014	0.0574847	99.73938
Dim.78	0.0917010	0.0529249	99.79230
Dim.79	0.0875116	0.0505071	99.84281
Dim.80	0.0820157	0.0473351	99.89015
Dim.81	0.0798054	0.0460594	99.93620
Dim.82	0.0742665	0.0428627	99.97907
Dim.83	0.0362668	0.0209313	100.00000
Dim.84	0.0000019	0.0000011	100.00000

# scores plot
fviz_pca_ind(PCA.DC_imp_metabind_clust_log2)

Manual scores plot

# manual scores plot
(PCA_withQCs <- PC_coord_QC_log2 %>%
  ggplot(aes(x = Dim.1, y = Dim.2,
             fill = factor(treatment, levels = c("control", "beta", "red", "QC")),
             text = sample)) +
  geom_point(shape = 21, alpha = 0.8) +
  scale_fill_manual(values = c("lightgreen", "orange", "tomato", "lightgray")) +
  scale_color_manual(values = "black") +  
  theme_minimal() +
  coord_fixed(PC2_withQC/PC1_withQC) +
  labs(x = glue::glue("PC1: {PC1_withQC}%"),
       y = glue::glue("PC2: {PC2_withQC}%"),
       fill = "Group",
       title = "Principal Components Analysis Scores Plot",
       subtitle = "Log2 transformed data"))

ggplotly(PCA_withQCs, tooltip = "text")

Without QCs

Wrangle

DC_imp_metabind_clust_log2_noQCs <- DC_imp_metabind_clust_log2 %>%
  filter(treatment != "QC")

PCA.DC_imp_metabind_clust_log2_noQCs <- PCA(DC_imp_metabind_clust_log2_noQCs, # wide data
                               quali.sup=1:14, # remove qualitative variables
                               graph=FALSE, # don't graph
                               scale.unit=FALSE) # don't scale, we already did this

# look at summary
kable(summary(PCA.DC_imp_metabind_clust_log2_noQCs))

## 
## Call:
## PCA(X = DC_imp_metabind_clust_log2_noQCs, scale.unit = FALSE,  
##      quali.sup = 1:14, graph = FALSE) 
## 
## 
## Eigenvalues
##                        Dim.1   Dim.2   Dim.3   Dim.4   Dim.5   Dim.6   Dim.7
## Variance              21.617  16.532  14.989  13.437  10.636   9.166   8.369
## % of var.             11.456   8.761   7.943   7.121   5.637   4.858   4.435
## Cumulative % of var.  11.456  20.217  28.161  35.281  40.918  45.776  50.211
##                        Dim.8   Dim.9  Dim.10  Dim.11  Dim.12  Dim.13  Dim.14
## Variance               7.644   7.304   6.151   5.734   5.193   4.398   3.791
## % of var.              4.051   3.871   3.260   3.039   2.752   2.331   2.009
## Cumulative % of var.  54.261  58.132  61.392  64.431  67.183  69.514  71.523
##                       Dim.15  Dim.16  Dim.17  Dim.18  Dim.19  Dim.20  Dim.21
## Variance               3.678   3.434   3.205   3.053   2.653   2.420   2.342
## % of var.              1.949   1.820   1.698   1.618   1.406   1.282   1.241
## Cumulative % of var.  73.472  75.292  76.991  78.609  80.015  81.297  82.539
##                       Dim.22  Dim.23  Dim.24  Dim.25  Dim.26  Dim.27  Dim.28
## Variance               2.245   1.951   1.734   1.649   1.511   1.447   1.378
## % of var.              1.190   1.034   0.919   0.874   0.801   0.767   0.730
## Cumulative % of var.  83.729  84.762  85.681  86.555  87.356  88.122  88.852
##                       Dim.29  Dim.30  Dim.31  Dim.32  Dim.33  Dim.34  Dim.35
## Variance               1.331   1.158   1.147   1.031   0.999   0.960   0.888
## % of var.              0.705   0.614   0.608   0.546   0.530   0.509   0.471
## Cumulative % of var.  89.558  90.172  90.780  91.326  91.855  92.364  92.835
##                       Dim.36  Dim.37  Dim.38  Dim.39  Dim.40  Dim.41  Dim.42
## Variance               0.880   0.778   0.743   0.695   0.665   0.601   0.584
## % of var.              0.466   0.412   0.394   0.368   0.352   0.318   0.310
## Cumulative % of var.  93.301  93.713  94.107  94.475  94.827  95.146  95.455
##                       Dim.43  Dim.44  Dim.45  Dim.46  Dim.47  Dim.48  Dim.49
## Variance               0.536   0.521   0.517   0.473   0.458   0.430   0.401
## % of var.              0.284   0.276   0.274   0.251   0.242   0.228   0.212
## Cumulative % of var.  95.739  96.015  96.289  96.540  96.782  97.010  97.222
##                       Dim.50  Dim.51  Dim.52  Dim.53  Dim.54  Dim.55  Dim.56
## Variance               0.378   0.376   0.349   0.343   0.329   0.323   0.314
## % of var.              0.200   0.199   0.185   0.182   0.174   0.171   0.167
## Cumulative % of var.  97.422  97.622  97.807  97.989  98.163  98.334  98.501
##                       Dim.57  Dim.58  Dim.59  Dim.60  Dim.61  Dim.62  Dim.63
## Variance               0.294   0.278   0.261   0.241   0.232   0.230   0.219
## % of var.              0.156   0.148   0.138   0.128   0.123   0.122   0.116
## Cumulative % of var.  98.656  98.804  98.942  99.070  99.193  99.315  99.431
##                       Dim.64  Dim.65  Dim.66  Dim.67  Dim.68  Dim.69
## Variance               0.199   0.189   0.185   0.173   0.168   0.160
## % of var.              0.106   0.100   0.098   0.092   0.089   0.085
## Cumulative % of var.  99.537  99.637  99.735  99.827  99.915 100.000
## 
## Individuals (the 10 first)
##                       Dist     Dim.1     ctr    cos2     Dim.2     ctr    cos2
## 1                |  20.626 |  -2.350   0.365   0.013 | -10.222   9.030   0.246
## 2                |  16.336 |   5.053   1.687   0.096 |  -4.493   1.744   0.076
## 3                |  13.939 |  -3.381   0.756   0.059 |  -1.995   0.344   0.020
## 4                |  17.543 | -11.161   8.232   0.405 |  -3.795   1.245   0.047
## 5                |  13.107 |  -1.471   0.143   0.013 |  -3.236   0.905   0.061
## 6                |  12.476 |   3.078   0.626   0.061 |  -2.187   0.413   0.031
## 7                |  13.498 |  -6.406   2.712   0.225 |   3.774   1.231   0.078
## 8                |  21.675 | -16.885  18.840   0.607 |   3.827   1.266   0.031
## 9                |  15.910 |   3.208   0.680   0.041 |  -6.731   3.915   0.179
## 10               |  14.902 |   6.384   2.693   0.184 |  -2.210   0.422   0.022
##                      Dim.3     ctr    cos2  
## 1                |   4.094   1.598   0.039 |
## 2                |   0.460   0.020   0.001 |
## 3                |  -4.606   2.022   0.109 |
## 4                |   2.888   0.795   0.027 |
## 5                |   1.140   0.124   0.008 |
## 6                |  -4.520   1.948   0.131 |
## 7                |  -8.348   6.642   0.383 |
## 8                |  -6.209   3.674   0.082 |
## 9                |  -1.775   0.300   0.012 |
## 10               |  -3.963   1.497   0.071 |
## 
## Variables (the 10 first)
##                     Dim.1    ctr   cos2    Dim.2    ctr   cos2    Dim.3    ctr
## 272.9587_0.485   | -0.023  0.002  0.016 |  0.015  0.001  0.007 | -0.026  0.005
## 288.9363_0.485   | -0.015  0.001  0.009 |  0.034  0.007  0.047 |  0.047  0.014
## 226.9658_0.499   |  0.003  0.000  0.001 |  0.005  0.000  0.002 |  0.000  0.000
## 294.9532_0.5     |  0.018  0.001  0.023 |  0.009  0.000  0.005 |  0.019  0.002
## 362.9406_0.501   |  0.005  0.000  0.003 |  0.000  0.000  0.000 | -0.014  0.001
## 520.9099_0.514   |  0.017  0.001  0.045 | -0.019  0.002  0.060 |  0.009  0.001
## 588.8973_0.514   |  0.016  0.001  0.041 | -0.015  0.001  0.034 | -0.018  0.002
## 452.9225_0.515   |  0.012  0.001  0.036 | -0.015  0.001  0.056 |  0.005  0.000
## 656.8848_0.515   |  0.030  0.004  0.076 | -0.029  0.005  0.070 | -0.008  0.000
## 724.8722_0.515   |  0.019  0.002  0.035 | -0.023  0.003  0.053 | -0.024  0.004
##                    cos2  
## 272.9587_0.485    0.021 |
## 288.9363_0.485    0.087 |
## 226.9658_0.499    0.000 |
## 294.9532_0.5      0.027 |
## 362.9406_0.501    0.022 |
## 520.9099_0.514    0.013 |
## 588.8973_0.514    0.047 |
## 452.9225_0.515    0.006 |
## 656.8848_0.515    0.005 |
## 724.8722_0.515    0.054 |
## 
## Supplementary categories (the 10 first)
##                      Dist    Dim.1   cos2 v.test    Dim.2   cos2 v.test  
## 5101             | 10.150 |  1.395  0.019  0.427 | -0.524  0.003 -0.183 |
## 5102             | 10.416 | -2.515  0.058 -0.770 |  4.671  0.201  1.637 |
## 5103             | 12.264 | -5.713  0.217 -1.750 |  0.154  0.000  0.054 |
## 5104             | 11.605 | -8.031  0.479 -2.461 |  1.904  0.027  0.667 |
## 5105             | 13.384 | -6.744  0.254 -2.066 |  0.113  0.000  0.040 |
## 5107             | 17.064 |  1.352  0.006  0.414 | -7.358  0.186 -2.578 |
## 5108             | 14.342 | -2.984  0.043 -0.914 | -6.401  0.199 -2.243 |
## 5109             | 13.984 | -7.271  0.270 -2.228 | -2.895  0.043 -1.014 |
## 5110             | 11.507 | -0.782  0.005 -0.240 | -4.276  0.138 -1.498 |
## 5111             | 12.772 | -2.192  0.029 -0.672 |  8.115  0.404  2.843 |
##                   Dim.3   cos2 v.test  
## 5101             -0.963  0.009 -0.354 |
## 5102              1.398  0.018  0.514 |
## 5103             -4.590  0.140 -1.689 |
## 5104             -1.593  0.019 -0.586 |
## 5105             -3.762  0.079 -1.384 |
## 5107              2.277  0.018  0.838 |
## 5108             -4.617  0.104 -1.699 |
## 5109             -0.859  0.004 -0.316 |
## 5110             -1.166  0.010 -0.429 |
## 5111             -2.251  0.031 -0.828 |

		Dist		Dim.1	cos2	v.test		Dim.2	cos2	v.test		Dim.3	cos2	v.test
5101	|	10.150	|	1.395	0.019	0.427	|	-0.524	0.003	-0.183	|	-0.963	0.009	-0.354	|
5102	|	10.416	|	-2.515	0.058	-0.770	|	4.671	0.201	1.637	|	1.398	0.018	0.514	|
5103	|	12.264	|	-5.713	0.217	-1.750	|	0.154	0.000	0.054	|	-4.590	0.140	-1.689	|
5104	|	11.605	|	-8.031	0.479	-2.461	|	1.904	0.027	0.667	|	-1.593	0.019	-0.586	|
5105	|	13.384	|	-6.744	0.254	-2.066	|	0.113	0.000	0.040	|	-3.762	0.079	-1.384	|
5107	|	17.064	|	1.352	0.006	0.414	|	-7.358	0.186	-2.578	|	2.277	0.018	0.838	|
5108	|	14.342	|	-2.984	0.043	-0.914	|	-6.401	0.199	-2.243	|	-4.617	0.104	-1.699	|
5109	|	13.984	|	-7.271	0.270	-2.228	|	-2.895	0.043	-1.014	|	-0.859	0.004	-0.316	|
5110	|	11.507	|	-0.782	0.005	-0.240	|	-4.276	0.138	-1.498	|	-1.166	0.010	-0.429	|
5111	|	12.772	|	-2.192	0.029	-0.672	|	8.115	0.404	2.843	|	-2.251	0.031	-0.828	|

# pull PC coordinates into df
PC_coord_noQCs_log2 <- as.data.frame(PCA.DC_imp_metabind_clust_log2_noQCs$ind$coord)

# bind back metadata from cols 1-14
PC_coord_noQCs_log2 <- bind_cols(DC_imp_metabind_clust_log2_noQCs[,1:14], PC_coord_noQCs_log2)

# grab some variance explained
importance_noQC <- PCA.DC_imp_metabind_clust_log2_noQCs$eig

# set variance explained with PC1, round to 2 digits
PC1_noQC <- round(importance_noQC[1,2], 2)

# set variance explained with PC2, round to 2 digits
PC2_noQC <- round(importance_noQC[2,2], 2)

Plots

Using FactoExtra

# scree plot
fviz_eig(PCA.DC_imp_metabind_clust_log2_noQCs)

# scores plot
fviz_pca_ind(PCA.DC_imp_metabind_clust_log2_noQCs)

Manual scores plot

# wrangling data prior to plot for ease
PC_coord_noQCs_log2 <- PC_coord_noQCs_log2 %>%
  mutate(sample2 = sample) %>%
  mutate_at("sample2", str_sub, start=7, end=8) %>%
  mutate(period = sample2) %>%
  unite(treatment_period, "treatment", "period", sep = "_", remove = FALSE) %>%
  dplyr::select(!sample2) %>%
  mutate_at("period", as.factor) %>%
  # relevel factors
  mutate(treatment_period = fct_relevel(treatment_period, c("control_b1", "control_b3", 
                                                            "beta_b1", "beta_b3",
                                                            "red_b1", "red_b3")),
         treatment = fct_relevel(treatment, c("control", "beta", "red")))

(PCA_withoutQCs <- PC_coord_noQCs_log2 %>%
   ggplot(aes(x = Dim.1, y = Dim.2,
             fill = treatment_period,
             text = sample)) +
   geom_point(shape = 21, alpha = 0.8) +
   geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
   geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
  scale_fill_manual(values = c("darkseagreen2", "darkgreen", 
                               "tan3", "orangered2",
                               "lavenderblush3", "darkred")) +
  scale_color_manual(values = "black") +  
  theme_minimal() +
  coord_fixed(PC2_noQC/PC1_noQC) +
  labs(x = glue::glue("PC1: {PC1_noQC}%"),
       y = glue::glue("PC2: {PC2_noQC}%"),
       fill = "Group",
       title = "Principal Components Analysis Scores Plot",
       subtitle = "Log2 transformed data, No QCs"))

ggplotly(PCA_withoutQCs, tooltip = "text")

Faceted by period

(PCA_faceted_noQCs <- PC_coord_noQCs_log2 %>%
   ggplot(aes(x = Dim.1, y = Dim.2,
             fill = treatment_period,
             text = sample)) +
   geom_point(shape = 21, alpha = 0.8) +
   geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
   geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
  scale_fill_manual(values = c("darkseagreen2", "darkgreen", 
                               "tan3", "orangered2",
                               "lavenderblush3", "darkred")) +
  scale_color_manual(values = "black") +  
  theme_bw() +
  coord_fixed(PC2_noQC/PC1_noQC) +
  labs(x = glue::glue("PC1: {PC1_noQC}%"),
       y = glue::glue("PC2: {PC2_noQC}%"),
       fill = "treatment_period",
       title = "Principal Components Analysis Scores Plot",
       subtitle = "Log2 transformed data, No QCs") +
  facet_wrap( ~ period) +
   theme(strip.background = element_rect(fill="white")))

ggplotly(PCA_faceted_noQCs, tooltip = "text")

PC_coord_noQCs_log2 %>%
   ggplot(aes(x = Dim.1, y = Dim.2,
             fill = sex,
             text = sample)) +
   geom_point(shape = 21, alpha = 0.8) +
   geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
   geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
  scale_fill_manual(values = c("purple", "blue")) +
  scale_color_manual(values = "black") +  
  theme_bw() +
  coord_fixed(PC2_noQC/PC1_noQC) +
  labs(x = glue::glue("PC1: {PC1_noQC}%"),
       y = glue::glue("PC2: {PC2_noQC}%"),
       fill = "sex",
       title = "Principal Components Analysis Scores Plot",
       subtitle = "Log2 transformed data, No QCs") +
  facet_wrap( ~ period) +
   theme(strip.background = element_rect(fill="white"))

Faceted by trt

trt_labels <- c("control" = "Control",
                "beta" = "High \U03b2-Carotene", 
                "red" = "High Lycopene")

(PCA_faceted_noQCs2 <- PC_coord_noQCs_log2 %>%
   ggplot(aes(x = Dim.1, y = Dim.2,
             fill = treatment_period,
             text = sample)) +
   geom_point(shape = 21, alpha = 0.8) +
   geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
   geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
  scale_fill_manual(values = c("darkseagreen2", "darkgreen", 
                               "tan", "orangered2",
                               "lavenderblush3", "darkred"),
                    labels = c("pre control", "post control",
                               "pre high \U03b2-carotene", "post high \U03b2-carotene",
                               "pre high lycopene", "post high lycopene")) +
  scale_color_manual(values = "black") +  
  theme_bw() +
  coord_fixed(PC2_noQC/PC1_noQC) +
  labs(x = glue::glue("PC1: {PC1_noQC}%"),
       y = glue::glue("PC2: {PC2_noQC}%"),
       fill = "Intervention timepoint",
       title = "Principal Components Analysis Scores Plot",
       subtitle = "Log2 transformed LC-MS lipidomics data (ESI -) faceted by treatment group") +
   facet_wrap( ~ treatment, labeller = as_labeller(trt_labels)))

Export plot

ggsave(plot = PCA_faceted_noQCs2,
       filename = "plots and figures/PCA-faceted-by-group-lipidomicsNEG.svg",
       bg = "transparent",
       height = 4,
       width = 8)

PC_coord_noQCs_log2 %>%
   ggplot(aes(x = Dim.1, y = Dim.2,
             fill = sex,
             text = sample)) +
   geom_point(shape = 21, alpha = 0.8) +
   geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
   geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
  scale_fill_manual(values = c("purple", "blue")) +
  scale_color_manual(values = "black") +  
  theme_bw() +
  coord_fixed(PC2_noQC/PC1_noQC) +
  labs(x = glue::glue("PC1: {PC1_noQC}%"),
       y = glue::glue("PC2: {PC2_noQC}%"),
       fill = "sex",
       title = "Principal Components Analysis Scores Plot",
       subtitle = "Log2 transformed data, No QCs") +
  facet_wrap( ~ treatment) +
   theme(strip.background = element_rect(fill="white"))

Faceted by sex

(PC_coord_facetsex <- PC_coord_noQCs_log2 %>%
   ggplot(aes(x = Dim.1, y = Dim.2,
             fill = treatment_period,
             text = sample)) +
   geom_point(shape = 21, alpha = 0.8) +
   geom_hline(yintercept = 0, linetype = "dashed", alpha=0.5) +
   geom_vline(xintercept = 0, linetype = "dashed", alpha=0.5) +
  scale_fill_manual(values = c("darkseagreen2", "darkgreen", 
                               "tan3", "orangered2",
                               "lavenderblush3", "darkred")) +
  scale_color_manual(values = "black") +  
  theme_bw() +
  coord_fixed(PC2_noQC/PC1_noQC) +
  labs(x = glue::glue("PC1: {PC1_noQC}%"),
       y = glue::glue("PC2: {PC2_noQC}%"),
       fill = "sex",
       title = "Principal Components Analysis Scores Plot",
       subtitle = "Log2 transformed data, No QCs") +
  facet_wrap( ~ sex) +
   theme(strip.background = element_rect(fill="white")))

ggplotly(PC_coord_facetsex, tooltip = "text")

Multilevel PCA

This type of PCA accounts for the structure of paired data, allowing for a more accurate assessment of biological differences between treatment groups, not differences attributed to the natural variation between individuals.

See http://mixomics.org/methods/multilevel/ for more info.

Wrangle

Data_forMPCA <- DC_imp_metabind_clust_log2_noQCs %>%
  mutate_at("subject", as.factor) %>%
  mutate(sample2 = sample,
         b_c = carotenoids$b_c_nmol_l_plasma,
         lyc = carotenoids$lyc_nmol_l_plasma,
         apo13one = carotenoids$apo13one_nmol_l_plasma,
         retinol = carotenoids$retinol_nmol_l_plasma,
         total_carot = carotenoids$total_carotenoids) %>%
  mutate_at("sample2", str_sub, start=7, end=8) %>%
  mutate(period = sample2) %>%
  dplyr::select(c(1:14), period, b_c, lyc, apo13one, retinol, total_carot, everything()) %>%
  dplyr::select(!sample2)
 

summary(as.factor(Data_forMPCA$subject))

## 5101 5102 5103 5104 5105 5107 5108 5109 5110 5111 5112 5113 5114 5115 5116 5117 
##    2    2    2    2    2    2    2    2    2    2    2    2    2    2    2    2 
## 5118 5119 5120 5121 5122 5123 5124 5125 5126 5127 5128 5129 5130 5131 5132 5133 
##    2    2    2    2    2    2    2    2    2    2    2    2    2    2    2    2 
## 5134 5135 5136 
##    2    2    2

# make a vector for meta variables
(metavar <- Data_forMPCA[,c(1:20)] %>%
    colnames())

##  [1] "subject"           "treatment"         "tomato_or_control"
##  [4] "sex"               "bmi"               "age"              
##  [7] "tot_chol"          "ldl_chol"          "hdl_chol"         
## [10] "triglycerides"     "glucose"           "SBP"              
## [13] "DBP"               "sample"            "period"           
## [16] "b_c"               "lyc"               "apo13one"         
## [19] "retinol"           "total_carot"

Regular PCA

mixOmicsPCA.result <- mixOmics::pca(Data_forMPCA[,!names(Data_forMPCA) %in% metavar],
                            scale = FALSE,
                            center = FALSE)

plotIndiv(mixOmicsPCA.result, 
          ind.names = Data_forMPCA$subject, 
          group = Data_forMPCA$treatment, 
          legend = TRUE, 
          legend.title = "Treatment", 
          title = 'Regular PCA, Lipidomics C18 (-)')

Multilevel PCA

With all data

multilevelPCA.result <- mixOmics::pca(Data_forMPCA[,-(c(1:20))], 
                            multilevel = Data_forMPCA$subject,
                            scale = FALSE,
                            center = FALSE,ncomp = 10)

plotIndiv(multilevelPCA.result, 
          ind.names = Data_forMPCA$period, 
          group = Data_forMPCA$treatment, 
          legend = TRUE, 
          legend.title = "Treatment", 
          title = 'Multilevel PCA, Lipidomics C18 (-)',comp = c(1,2))

Loadings

plotLoadings(multilevelPCA.result, ndisplay = 30)

sPLS-DA

Following http://mixomics.org/case-studies/splsda-srbct-case-study/

mat_Data_forMPCA <- Data_forMPCA %>%
  dplyr::select(!metavar) %>%
  as.matrix()
  

class_forMPCA <- Data_forMPCA$treatment %>%
  as.factor()

# checking dimensions of feature abundance matrix
dim(mat_Data_forMPCA)

# checking the distribution of treatment group
summary(class_forMPCA)

Initial model

“A PLS-DA model is fitted with ten components to evaluate the performance and the number of components necessary for the final model.”

splsda <- splsda(mat_Data_forMPCA, class_forMPCA, ncomp = 3)  # set ncomp to 10 for performance assessment later

# plot the samples projected onto the first two components of the PLS-DA subspace
plotIndiv(splsda , comp = 1:2, 
          group = class_forMPCA, ind.names = FALSE,  # colour points by class
          ellipse = TRUE, # include 95% confidence ellipse for each class
          legend = TRUE, title = '(a) PLSDA with confidence ellipses')

# use the max.dist measure to form decision boundaries between classes based on PLS-DA data
background = background.predict(splsda, comp.predicted=2, dist = "max.dist")

# plot the samples projected onto the first two components of the PLS-DA subspace
plotIndiv(splsda, comp = 1:2,
          group = class_forMPCA, ind.names = FALSE, # colour points by class
          background = background, # include prediction background for each class
          legend = TRUE, title = " (b) PLSDA with prediction background")

Model tuning

# undergo performance evaluation in order to tune the number of components to use
perf.splsda <- perf(splsda, validation = "loo", 
                          folds = 5, nrepeat = 100, # use repeated cross-validation
                          progressBar = FALSE, auc = TRUE) # include AUC values

# plot the outcome of performance evaluation across all ten components
plot(perf.splsda, col = color.mixo(5:7), sd = TRUE,
     legend.position = "horizontal")

perf.splsda$choice.ncomp # what is the optimal value of components according to perf()

# grid of possible keepX values that will be tested for each component
list.keepX <- c(1:10,  seq(20, 300, 10))

# undergo the tuning process to determine the optimal number of variables
tune.splsda <- tune.splsda(mat_Data_forMPCA, class_forMPCA, ncomp = 9, # calculate for first 9 components
                                 validation = 'loo',
                                 folds = 5, nrepeat = 100, # use repeated cross-validation
                                 dist = 'max.dist', # use max.dist measure
                                 measure = "BER", # use balanced error rate of dist measure
                                 test.keepX = list.keepX,
                                 cpus = 2) # allow for parallelization to decrease runtime

plot(tune.splsda, col = color.jet(9)) # plot output of variable number tuning

sPCA

result.sPCA.multi <- spca(Data_forMPCA[,!names(Data_forMPCA) %in% metavar],
                          keepX = c(10, 10),
                          multilevel = Data_forMPCA$subject)


plotIndiv(result.sPCA.multi,
          ind.names = Data_forMPCA$period, 
          group = Data_forMPCA$treatment, 
          legend = TRUE, 
          legend.title = "Treatment", 
          title = 'Multilevel sPCA, Lipidomics C18 (-)',comp = c(1,2))

plotVar(result.sPCA.multi)  

selectVar(result.sPCA.multi, comp = 1)$name

##  [1] "855.6591_9.033" "857.6749_9.986" "829.6431_8.816" "925.6617_9.988"
##  [5] "843.6586_9.396" "827.6272_7.905" "923.646_9.035"  "819.6141_8.816"
##  [9] "849.6462_9.988" "909.6368_9.99"

plotLoadings(result.sPCA.multi, method = "mean", contrib = "max")

PCAtools

Data wrangling

# create rel abund df suitable for PCAtools package
imp_clust_omicsdata_forPCAtools <- Data_forMPCA %>%
  # select only sample ID and feature columns
  dplyr::select(sample,
                21:ncol(.)) %>%
  # transpose
  t() %>%
  # convert back to df
  as.data.frame()

names(imp_clust_omicsdata_forPCAtools) <- imp_clust_omicsdata_forPCAtools[1,] # make samp;e IDs column names

imp_clust_omicsdata_forPCAtools <- imp_clust_omicsdata_forPCAtools[-1,] # remove sample ID row

# create metadata df suitable for PCAtools pckg

metadata_forPCAtools <- Data_forMPCA[,1:20]

metadata_forPCAtools <- metadata_forPCAtools %>%
  unite("treatment_period", c("treatment", "period"), sep = "_", remove = FALSE) %>%
  column_to_rownames("sample")
  

# create a vector so that col names in abundance df matches metadata df
order_forPCAtools <- match(colnames(imp_clust_omicsdata_forPCAtools), rownames(metadata_forPCAtools))

# reorder col names in abundance df so that it matches metadata
log2_abundances_reordered_forPCAtools <- imp_clust_omicsdata_forPCAtools[,order_forPCAtools] %>%
  # change abundance df to numeric
  mutate_all(as.numeric)

PCA

colby trt

# pca
p <- PCAtools::pca(log2_abundances_reordered_forPCAtools,
                   metadata = metadata_forPCAtools,
                   scale = FALSE, # using scaled data already (log2 transformed)
         
)

biplot(p, 
       lab = paste0(metadata_forPCAtools$subject),
       colby = 'treatment',
       colkey = c("control" = "lightgreen",
                  "beta" = "orange",
                  "red" = "tomato"),
       hline = 0, vline = 0,
       legendPosition = 'right',
       title = "PCA Scores Plot with Loadings",
       subtitle = "Log2 transformed data. 95% CI ellipses",
       ellipse = TRUE,
       ellipseType = 't', # assumes multivariate
       ellipseLevel = 0.95,
       ellipseFill = TRUE,
       ellipseAlpha = 0.2,
       ellipseLineSize = 0,
       showLoadings = TRUE,ntopLoadings = 10)

colby period

# pca

biplot(p,
       lab = paste0(metadata_forPCAtools$subject),
       colby = 'period',
       colkey = c("b1" = "gray",
                  "b3" = "pink"),
       hline = 0, vline = 0,
       legendPosition = 'right',
       title = "PCA Scores Plot with Loadings",
       subtitle = "Log2 transformed data. 95% CI ellipses",
       ellipse = TRUE,
       ellipseType = 't', # assumes multivariate
       ellipseLevel = 0.95,
       ellipseFill = TRUE,
       ellipseAlpha = 0.2,
       ellipseLineSize = 0,
       showLoadings = TRUE)

colby trt_period

biplot(p,
       lab = paste0(metadata_forPCAtools$subject),
       colby = 'treatment_period',
       colkey = c("control_b1" = "darkseagreen2",
               "control_b3" = "darkgreen",
               "beta_b1" = "tan3",
               "beta_b3" = "orangered2",
               "red_b1" = "lavenderblush3",
               "red_b3" = "darkred"),
       hline = 0, vline = 0,
       legendPosition = 'right',
       title = "PCA Scores Plot with Loadings",
       subtitle = "Log2 transformed data. 95% CI ellipses.",
       ellipse = TRUE,
       ellipseType = 't', # assumes multivariate
       ellipseLevel = 0.95,
       ellipseFill = TRUE,
       ellipseAlpha = 0.2,
       ellipseLineSize = 0,
       showLoadings = TRUE, ntopLoadings = 10)

biplot(p,
       lab = paste0(metadata_forPCAtools$subject),
       colby = 'treatment_period',
       colkey = c("control_b1" = "darkseagreen2",
               "control_b3" = "darkgreen",
               "beta_b1" = "tan3",
               "beta_b3" = "orangered2",
               "red_b1" = "lavenderblush3",
               "red_b3" = "darkred"),
       hline = 0, vline = 0,
       legendPosition = 'right',
       title = "PCA Scores Plot with Loadings",
       subtitle = "Log2 transformed data. 95% CI ellipses.",
       showLoadings = TRUE, ntopLoadings = 10)

Let’s explore a little more

How many PCs do we need to capture at least 80% variance?

which(cumsum(p$variance) > 80)[1]

## PC19 
##   19

This shows we’d need quite a few PCs to capture most of the variance.

Pairs plots

Here, we will look at separations for several components at once using pairs plots.

treatment_period

pairsplot(p,
    components = getComponents(p, c(1:10)),
    triangle = TRUE, trianglelabSize = 12,
    hline = 0, vline = 0,
    pointSize = 0.4,
    gridlines.major = FALSE, gridlines.minor = FALSE,
    colby = 'treatment_period',
    colkey = c("control_b1" = "darkseagreen2",
               "control_b3" = "darkgreen",
               "beta_b1" = "tan",
               "beta_b3" = "orangered2",
               "red_b1" = "lavenderblush3",
               "red_b3" = "darkred"),
    title = 'Pairs plot', plotaxes = FALSE,
    margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm'))

period

pairsplot(p,
    components = getComponents(p, c(1:10)),
    triangle = TRUE, trianglelabSize = 12,
    hline = 0, vline = 0,
    pointSize = 0.4,
    gridlines.major = FALSE, gridlines.minor = FALSE,
    colby = 'period',
    colkey = c("b1" = "darkgray",
               "b3" = "pink"),
    title = 'Pairs plot', plotaxes = FALSE,
    margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm'))

sex

Are there any obvious clusterings when colored by sex?

  pairsplot(p,
    components = getComponents(p, c(1:10)),
    triangle = TRUE, trianglelabSize = 12,
    hline = 0, vline = 0,
    pointSize = 0.4,
    gridlines.major = FALSE, gridlines.minor = FALSE,
    colby = 'sex', 
    colkey = c("M" = "red",
               "F" = "purple"),
    title = 'Pairs plot', plotaxes = FALSE,
    margingaps = unit(c(-0.01, -0.01, -0.01, -0.01), 'cm'))

Eigen corplots

This is a cool way to explore the correlations between the metadata and the PCs! I want to look at how the metavariables correlate with PCs that account for 80% variation in the dataset.

Again: How many PCs do we need to capture at least 80% variance?

which(cumsum(p$variance) > 80)[1]

## PC19 
##   19

  eigencorplot(p,
    components = getComponents(p, 1:which(cumsum(p$variance) > 80)[1]), # get components that account for 80% variance
    metavars = colnames(metadata_forPCAtools),
    col = c('darkblue', 'blue2', 'gray', 'red2', 'darkred'),
    cexCorval = 0.7,
    colCorval = 'white',
    fontCorval = 2,
    posLab = 'bottomleft',
    rotLabX = 45,
    posColKey = 'top',
    cexLabColKey = 1.5,
    scale = TRUE,
    main = 'PC1-15 metadata correlations',
    colFrame = 'white',
    plotRsquared = FALSE)

  eigencorplot(p,
    components = getComponents(p, 1:which(cumsum(p$variance) > 80)[1]),
    metavars = colnames(metadata_forPCAtools),
    col = c('white', 'cornsilk1', 'gold', 'forestgreen', 'darkgreen'),
    cexCorval = 1.2,
    fontCorval = 2,
    posLab = 'all',
    rotLabX = 45,
    scale = TRUE,
    main = bquote(Principal ~ component ~ Spearman ~ r^2 ~ metadata ~ correlates),
    plotRsquared = TRUE,
    corFUN = 'spearman',
    corUSE = 'pairwise.complete.obs',
    corMultipleTestCorrection = 'BH',
    signifSymbols = c('****', '***', '**', '*', ''),
    signifCutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1))

Univariate analysis

Wrangle data

# using the most convenient df to convert to tidy
df_for_stats <- Data_forMPCA %>%
  pivot_longer(21:ncol(.),
               names_to = "mz_rt",
               values_to = "rel_abund_log2") %>%
  unite("treatment_period", "treatment", "period", sep = "_", remove = FALSE) %>%
  mutate_at("treatment", as.factor)

# turn off sci notation outputs
options(scipen = 999)

Parametric tests

ANOVA across treatment timepoints

df_for_stats$treatment_period <- as.factor(df_for_stats$treatment_period)

trt_anova_output_df <- df_for_stats %>% 
  dplyr::select(subject, sample, treatment_period, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  anova_test(rel_abund_log2 ~ treatment_period, wid = subject,
             detailed = TRUE) %>%
  adjust_pvalue(method = "BH") %>%
  as.data.frame()


trt_anova_sig <- trt_anova_output_df %>%
  filter(p.adj < .05)

head(trt_anova_sig)

##              mz_rt           Effect    SSn    SSd DFn DFd     F        p p<.05
## 1 1334.2417_10.855 treatment_period  5.305 14.827   5  64 4.579 0.001000     *
## 2  1335.245_10.854 treatment_period  4.803 11.811   5  64 5.205 0.000453     *
## 3  1554.1226_7.773 treatment_period  1.342  3.604   5  64 4.766 0.000916     *
## 4  1561.2028_9.038 treatment_period  3.484 10.028   5  64 4.447 0.002000     *
## 5   201.0226_0.688 treatment_period 31.378 91.809   5  64 4.375 0.002000     *
## 6    244.908_0.706 treatment_period  4.986 13.922   5  64 4.585 0.001000     *
##     ges      p.adj
## 1 0.263 0.02890909
## 2 0.289 0.02274537
## 3 0.271 0.02890909
## 4 0.258 0.04892308
## 5 0.255 0.04892308
## 6 0.264 0.02890909

# how many significant features?
nrow(trt_anova_sig)

## [1] 39

trt_period_ANOVA_tukey <- df_for_stats %>% 
  dplyr::select(subject, sample, treatment_period, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  tukey_hsd(rel_abund_log2 ~ treatment_period, wid = subject)

trt_tukeyHSD_sig <- trt_period_ANOVA_tukey %>%
  filter(p.adj < .05)

t tests (unpaired and paired)

Paired control

# run paired t-tests for control intervention
ctrl_t.test_paired <- df_for_stats %>%
  filter(treatment == "control") %>%
 dplyr::select(subject, period, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ period, 
         paired = TRUE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()

Statistically significant features

# which features are significant?
ctrl_t.test_paired_sig <- ctrl_t.test_paired %>%
  filter(p < 0.05)
kable(ctrl_t.test_paired_sig)

mz_rt	.y.	group1	group2	n1	n2	statistic	df	p	p.signif
1008.5435_7.813	rel_abund_log2	b1	b3	11	11	-2.398326	10	0.0374000	*
1061.6776_3.089	rel_abund_log2	b1	b3	11	11	2.649027	10	0.0244000	*
1235.798_10.088	rel_abund_log2	b1	b3	11	11	2.718321	10	0.0216000	*
1279.8241_10.52	rel_abund_log2	b1	b3	11	11	3.889474	10	0.0030100	**
1334.2417_10.855	rel_abund_log2	b1	b3	11	11	3.970460	10	0.0026400	**
1335.245_10.854	rel_abund_log2	b1	b3	11	11	4.756644	10	0.0007720	***
1394.0685_5.684	rel_abund_log2	b1	b3	11	11	-2.994221	10	0.0135000	*
151.0261_0.612	rel_abund_log2	b1	b3	11	11	-3.352314	10	0.0073400	**
1554.1226_7.773	rel_abund_log2	b1	b3	11	11	5.268520	10	0.0003640	***
1555.1258_7.773	rel_abund_log2	b1	b3	11	11	5.041887	10	0.0005050	***
1598.1714_7.758	rel_abund_log2	b1	b3	11	11	3.278484	10	0.0083100	**
1600.0951_6.94	rel_abund_log2	b1	b3	11	11	-2.493591	10	0.0318000	*
1605.0902_6.358	rel_abund_log2	b1	b3	11	11	2.905978	10	0.0157000	*
1616.1136_7.768	rel_abund_log2	b1	b3	11	11	2.966338	10	0.0141000	*
1632.139_7.776	rel_abund_log2	b1	b3	11	11	3.619990	10	0.0046900	**
1633.1422_7.775	rel_abund_log2	b1	b3	11	11	4.370406	10	0.0014000	**
1637.3241_10.442	rel_abund_log2	b1	b3	11	11	-2.441127	10	0.0348000	*
1646.3475_10.439	rel_abund_log2	b1	b3	11	11	-4.320767	10	0.0015100	**
1654.1535_8.02	rel_abund_log2	b1	b3	11	11	-2.344590	10	0.0410000	*
1655.1569_8.02	rel_abund_log2	b1	b3	11	11	-3.128094	10	0.0107000	*
1664.3521_10.59	rel_abund_log2	b1	b3	11	11	3.587703	10	0.0049500	**
1665.3553_10.589	rel_abund_log2	b1	b3	11	11	4.222488	10	0.0017600	**
1666.3553_10.589	rel_abund_log2	b1	b3	11	11	3.570495	10	0.0050900	**
167.021_0.602	rel_abund_log2	b1	b3	11	11	-3.367841	10	0.0071500	**
1674.3803_10.59	rel_abund_log2	b1	b3	11	11	3.733095	10	0.0038900	**
1675.384_10.589	rel_abund_log2	b1	b3	11	11	3.328586	10	0.0076400	**
188.9862_0.644	rel_abund_log2	b1	b3	11	11	-4.406827	10	0.0013200	**
201.0226_0.688	rel_abund_log2	b1	b3	11	11	2.352607	10	0.0405000	*
203.0013_0.66	rel_abund_log2	b1	b3	11	11	-2.574806	10	0.0277000	*
213.0223_0.689	rel_abund_log2	b1	b3	11	11	-4.470479	10	0.0012000	**
239.0923_0.975	rel_abund_log2	b1	b3	11	11	2.481271	10	0.0325000	*
288.9363_0.485	rel_abund_log2	b1	b3	11	11	2.738739	10	0.0209000	*
291.0839_0.618	rel_abund_log2	b1	b3	11	11	2.850671	10	0.0172000	*
291.0953_0.67	rel_abund_log2	b1	b3	11	11	-3.203689	10	0.0094300	**
311.2224_2.465	rel_abund_log2	b1	b3	11	11	2.346546	10	0.0409000	*
352.0856_0.687	rel_abund_log2	b1	b3	11	11	-2.402798	10	0.0371000	*
362.9406_0.501	rel_abund_log2	b1	b3	11	11	-2.757099	10	0.0202000	*
383.1529_0.667	rel_abund_log2	b1	b3	11	11	-2.613511	10	0.0259000	*
391.2849_2.246	rel_abund_log2	b1	b3	11	11	3.636218	10	0.0045700	**
397.3681_5	rel_abund_log2	b1	b3	11	11	5.511253	10	0.0002580	***
411.3837_5.349	rel_abund_log2	b1	b3	11	11	4.115092	10	0.0020900	**
437.0542_0.688	rel_abund_log2	b1	b3	11	11	-4.544781	10	0.0010700	**
441.3942_4.718	rel_abund_log2	b1	b3	11	11	6.009725	10	0.0001300	***
462.1763_0.655	rel_abund_log2	b1	b3	11	11	-2.989895	10	0.0136000	*
506.3245_3.232	rel_abund_log2	b1	b3	11	11	2.988165	10	0.0136000	*
507.223_1.7	rel_abund_log2	b1	b3	11	11	-6.463711	10	0.0000722	****
507.223_2.273	rel_abund_log2	b1	b3	11	11	-5.077456	10	0.0004800	***
524.3351_3.376	rel_abund_log2	b1	b3	11	11	-3.404611	10	0.0067200	**
528.2631_0.691	rel_abund_log2	b1	b3	11	11	2.343265	10	0.0411000	*
539.4309_4.416	rel_abund_log2	b1	b3	11	11	2.944283	10	0.0147000	*
551.3582_4.841	rel_abund_log2	b1	b3	11	11	-8.525686	10	0.0000067	****
557.4566_6.309	rel_abund_log2	b1	b3	11	11	2.667967	10	0.0236000	*
557.457_6.865	rel_abund_log2	b1	b3	11	11	2.417936	10	0.0362000	*
559.4719_6.867	rel_abund_log2	b1	b3	11	11	3.441306	10	0.0063200	**
566.3462_3.232	rel_abund_log2	b1	b3	11	11	2.581398	10	0.0274000	*
567.4621_4.643	rel_abund_log2	b1	b3	11	11	6.876930	10	0.0000431	****
573.4515_4.266	rel_abund_log2	b1	b3	11	11	3.947276	10	0.0027400	**
573.4515_4.873	rel_abund_log2	b1	b3	11	11	4.352232	10	0.0014400	**
575.4672_5.354	rel_abund_log2	b1	b3	11	11	8.914095	10	0.0000045	****
575.467_4.662	rel_abund_log2	b1	b3	11	11	4.239741	10	0.0017200	**
577.3738_4.918	rel_abund_log2	b1	b3	11	11	-6.880566	10	0.0000429	****
579.3893_5.557	rel_abund_log2	b1	b3	11	11	-5.855048	10	0.0001610	***
583.1735_0.618	rel_abund_log2	b1	b3	11	11	2.363323	10	0.0397000	*
586.3143_2.221	rel_abund_log2	b1	b3	11	11	2.599947	10	0.0265000	*
591.3894_5.174	rel_abund_log2	b1	b3	11	11	-3.921007	10	0.0028600	**
591.462_4.314	rel_abund_log2	b1	b3	11	11	4.839268	10	0.0006820	***
591.462_4.651	rel_abund_log2	b1	b3	11	11	8.888686	10	0.0000046	****
593.4778_4.664	rel_abund_log2	b1	b3	11	11	9.251220	10	0.0000032	****
595.4933_4.953	rel_abund_log2	b1	b3	11	11	20.304358	10	0.0000000	****
605.4051_5.422	rel_abund_log2	b1	b3	11	11	2.490637	10	0.0320000	*
618.308_3.232	rel_abund_log2	b1	b3	11	11	2.650333	10	0.0243000	*
627.1374_0.611	rel_abund_log2	b1	b3	11	11	4.157237	10	0.0019600	**
628.5437_10.491	rel_abund_log2	b1	b3	11	11	4.029686	10	0.0024000	**
634.3329_3.232	rel_abund_log2	b1	b3	11	11	3.443726	10	0.0062900	**
654.5589_10.54	rel_abund_log2	b1	b3	11	11	2.657957	10	0.0240000	*
656.5586_10.54	rel_abund_log2	b1	b3	11	11	3.064140	10	0.0120000	*
656.575_10.694	rel_abund_log2	b1	b3	11	11	3.607274	10	0.0047900	**
658.0669_0.628	rel_abund_log2	b1	b3	11	11	2.624871	10	0.0254000	*
676.6243_10.731	rel_abund_log2	b1	b3	11	11	-2.512566	10	0.0308000	*
684.6065_10.853	rel_abund_log2	b1	b3	11	11	4.303188	10	0.0015500	**
686.6207_10.914	rel_abund_log2	b1	b3	11	11	2.313184	10	0.0433000	*
696.6064_10.749	rel_abund_log2	b1	b3	11	11	-2.245948	10	0.0485000	*
711.6251_10.854	rel_abund_log2	b1	b3	11	11	3.668546	10	0.0043300	**
716.5225_8.139	rel_abund_log2	b1	b3	11	11	3.611030	10	0.0047600	**
723.5203_6.092	rel_abund_log2	b1	b3	11	11	-3.259499	10	0.0085800	**
733.5494_6.091	rel_abund_log2	b1	b3	11	11	-2.753136	10	0.0204000	*
738.507_7.269	rel_abund_log2	b1	b3	11	11	2.284240	10	0.0455000	*
746.5966_10.853	rel_abund_log2	b1	b3	11	11	3.830675	10	0.0033200	**
752.5589_9.222	rel_abund_log2	b1	b3	11	11	-2.627420	10	0.0253000	*
759.5646_6.287	rel_abund_log2	b1	b3	11	11	-2.542591	10	0.0292000	*
761.5803_7.053	rel_abund_log2	b1	b3	11	11	-3.748263	10	0.0037900	**
762.5643_8.206	rel_abund_log2	b1	b3	11	11	-2.825006	10	0.0180000	*
762.6216_10.854	rel_abund_log2	b1	b3	11	11	3.082883	10	0.0116000	*
767.5664_7.562	rel_abund_log2	b1	b3	11	11	-4.147683	10	0.0019900	**
771.5646_6.096	rel_abund_log2	b1	b3	11	11	-4.694516	10	0.0008490	***
773.5806_6.749	rel_abund_log2	b1	b3	11	11	-2.764133	10	0.0200000	*
775.5965_7.563	rel_abund_log2	b1	b3	11	11	-3.234278	10	0.0089600	**
780.4705_7.867	rel_abund_log2	b1	b3	11	11	-2.657908	10	0.0240000	*
789.6114_7.911	rel_abund_log2	b1	b3	11	11	-2.243948	10	0.0487000	*
789.6116_8.177	rel_abund_log2	b1	b3	11	11	-3.164381	10	0.0101000	*
794.5464_7.772	rel_abund_log2	b1	b3	11	11	4.705066	10	0.0008350	***
796.5458_7.771	rel_abund_log2	b1	b3	11	11	4.727941	10	0.0008070	***
801.6117_7.833	rel_abund_log2	b1	b3	11	11	-2.452285	10	0.0341000	*
803.6275_8.771	rel_abund_log2	b1	b3	11	11	2.919414	10	0.0153000	*
804.5762_7.774	rel_abund_log2	b1	b3	11	11	5.108546	10	0.0004580	***
806.4942_7.271	rel_abund_log2	b1	b3	11	11	4.820188	10	0.0007020	***
810.5642_6.968	rel_abund_log2	b1	b3	11	11	2.602927	10	0.0264000	*
810.5647_7.467	rel_abund_log2	b1	b3	11	11	-3.368391	10	0.0071400	**
812.5436_6.468	rel_abund_log2	b1	b3	11	11	-2.338344	10	0.0415000	*
815.627_8.417	rel_abund_log2	b1	b3	11	11	-2.884629	10	0.0163000	*
818.6271_10.414	rel_abund_log2	b1	b3	11	11	4.481994	10	0.0011800	**
819.6141_8.816	rel_abund_log2	b1	b3	11	11	-2.278900	10	0.0459000	*
819.6141_9.024	rel_abund_log2	b1	b3	11	11	3.243826	10	0.0088100	**
820.5694_6.598	rel_abund_log2	b1	b3	11	11	2.232154	10	0.0497000	*
820.627_10.414	rel_abund_log2	b1	b3	11	11	4.813365	10	0.0007090	***
821.565_7.768	rel_abund_log2	b1	b3	11	11	3.640792	10	0.0045300	**
821.6139_9.026	rel_abund_log2	b1	b3	11	11	4.184462	10	0.0018700	**
821.6152_8.817	rel_abund_log2	b1	b3	11	11	-3.428282	10	0.0064600	**
822.5774_8.941	rel_abund_log2	b1	b3	11	11	3.295648	10	0.0080700	**
823.6294_9.979	rel_abund_log2	b1	b3	11	11	4.039762	10	0.0023600	**
824.5441_6.359	rel_abund_log2	b1	b3	11	11	2.355183	10	0.0403000	*
824.5789_7.841	rel_abund_log2	b1	b3	11	11	-2.439056	10	0.0349000	*
827.6272_7.905	rel_abund_log2	b1	b3	11	11	-4.564604	10	0.0010300	**
828.6559_10.414	rel_abund_log2	b1	b3	11	11	3.789411	10	0.0035500	**
829.6431_8.816	rel_abund_log2	b1	b3	11	11	-2.933059	10	0.0150000	*
829.6432_9.024	rel_abund_log2	b1	b3	11	11	4.768161	10	0.0007590	***
831.6576_9.28	rel_abund_log2	b1	b3	11	11	-2.923286	10	0.0152000	*
831.659_9.98	rel_abund_log2	b1	b3	11	11	3.935112	10	0.0028000	**
832.6067_8.94	rel_abund_log2	b1	b3	11	11	2.917804	10	0.0154000	*
832.6428_10.551	rel_abund_log2	b1	b3	11	11	-3.324458	10	0.0076900	**
833.5544_7.557	rel_abund_log2	b1	b3	11	11	-2.394152	10	0.0377000	*
833.6294_9.395	rel_abund_log2	b1	b3	11	11	-3.949641	10	0.0027300	**
833.6297_9.665	rel_abund_log2	b1	b3	11	11	-3.629255	10	0.0046200	**
834.5643_7.242	rel_abund_log2	b1	b3	11	11	-2.823948	10	0.0180000	*
835.5322_8.336	rel_abund_log2	b1	b3	11	11	3.124019	10	0.0108000	*
835.6457_10.444	rel_abund_log2	b1	b3	11	11	-2.576877	10	0.0276000	*
836.5434_6.295	rel_abund_log2	b1	b3	11	11	-2.726491	10	0.0213000	*
837.6571_10.552	rel_abund_log2	b1	b3	11	11	-3.234364	10	0.0089500	**
838.5955_8.279	rel_abund_log2	b1	b3	11	11	-2.469919	10	0.0331000	*
840.5749_7.321	rel_abund_log2	b1	b3	11	11	-2.468933	10	0.0332000	*
840.5749_7.471	rel_abund_log2	b1	b3	11	11	-3.126034	10	0.0108000	*
841.5661_6.75	rel_abund_log2	b1	b3	11	11	-5.215558	10	0.0003920	***
841.6406_8.452	rel_abund_log2	b1	b3	11	11	-3.891593	10	0.0030000	**
842.6715_10.551	rel_abund_log2	b1	b3	11	11	-5.432362	10	0.0002880	***
843.6586_9.396	rel_abund_log2	b1	b3	11	11	-3.554469	10	0.0052300	**
843.6587_9.666	rel_abund_log2	b1	b3	11	11	-4.167152	10	0.0019300	**
845.6746_10.439	rel_abund_log2	b1	b3	11	11	-2.415855	10	0.0363000	*
846.6588_10.643	rel_abund_log2	b1	b3	11	11	2.369900	10	0.0393000	*
847.6304_9.032	rel_abund_log2	b1	b3	11	11	-2.401890	10	0.0372000	*
848.5433_6.017	rel_abund_log2	b1	b3	11	11	-2.783976	10	0.0193000	*
849.6458_10.294	rel_abund_log2	b1	b3	11	11	3.160454	10	0.0102000	*
849.6616_10.589	rel_abund_log2	b1	b3	11	11	5.527135	10	0.0002520	***
851.6615_10.59	rel_abund_log2	b1	b3	11	11	3.179822	10	0.0098200	**
853.6428_8.12	rel_abund_log2	b1	b3	11	11	-3.100457	10	0.0112000	*
854.5918_8.021	rel_abund_log2	b1	b3	11	11	-3.629941	10	0.0046100	**
854.6714_10.418	rel_abund_log2	b1	b3	11	11	-2.280952	10	0.0457000	*
855.6591_9.033	rel_abund_log2	b1	b3	11	11	-3.253318	10	0.0086700	**
856.5377_7.769	rel_abund_log2	b1	b3	11	11	3.062711	10	0.0120000	*
857.5409_7.768	rel_abund_log2	b1	b3	11	11	2.862867	10	0.0169000	*
857.6737_9.513	rel_abund_log2	b1	b3	11	11	-3.964069	10	0.0026700	**
857.6746_10.294	rel_abund_log2	b1	b3	11	11	2.642620	10	0.0246000	*
859.6907_10.59	rel_abund_log2	b1	b3	11	11	4.237116	10	0.0017200	**
860.6369_10.19	rel_abund_log2	b1	b3	11	11	3.260431	10	0.0085700	**
860.6919_10.384	rel_abund_log2	b1	b3	11	11	-3.061764	10	0.0120000	*
862.5261_7.47	rel_abund_log2	b1	b3	11	11	-2.306507	10	0.0438000	*
862.527_7.471	rel_abund_log2	b1	b3	11	11	-3.520338	10	0.0055400	**
862.6535_10.583	rel_abund_log2	b1	b3	11	11	2.252514	10	0.0480000	*
863.6765_10.683	rel_abund_log2	b1	b3	11	11	-3.154368	10	0.0103000	*
864.6108_7.878	rel_abund_log2	b1	b3	11	11	-2.406403	10	0.0369000	*
865.6758_10.678	rel_abund_log2	b1	b3	11	11	-2.242872	10	0.0488000	*
869.598_7.834	rel_abund_log2	b1	b3	11	11	-2.578977	10	0.0275000	*
871.5502_7.054	rel_abund_log2	b1	b3	11	11	-2.674593	10	0.0233000	*
871.6891_10.337	rel_abund_log2	b1	b3	11	11	-3.911534	10	0.0029100	**
872.5626_7.769	rel_abund_log2	b1	b3	11	11	5.274071	10	0.0003610	***
872.5955_8.863	rel_abund_log2	b1	b3	11	11	-3.090235	10	0.0114000	*
872.6815_10.583	rel_abund_log2	b1	b3	11	11	4.914023	10	0.0006100	***
874.6643_9.99	rel_abund_log2	b1	b3	11	11	-2.660490	10	0.0239000	*
876.5748_6.891	rel_abund_log2	b1	b3	11	11	-3.067863	10	0.0119000	*
878.584_5.681	rel_abund_log2	b1	b3	11	11	-2.468788	10	0.0332000	*
878.5901_7.193	rel_abund_log2	b1	b3	11	11	-2.513654	10	0.0307000	*
878.5903_7.305	rel_abund_log2	b1	b3	11	11	-6.308136	10	0.0000882	****
878.5909_7.805	rel_abund_log2	b1	b3	11	11	-2.921379	10	0.0153000	*
881.6055_9.023	rel_abund_log2	b1	b3	11	11	3.636516	10	0.0045600	**
882.6209_8.855	rel_abund_log2	b1	b3	11	11	-2.424221	10	0.0358000	*
883.5331_7.753	rel_abund_log2	b1	b3	11	11	-4.792130	10	0.0007320	***
883.6211_9.976	rel_abund_log2	b1	b3	11	11	4.199866	10	0.0018300	**
884.5687_8.94	rel_abund_log2	b1	b3	11	11	2.422232	10	0.0359000	*
887.599_8.816	rel_abund_log2	b1	b3	11	11	-2.514231	10	0.0307000	*
890.5493_8.144	rel_abund_log2	b1	b3	11	11	2.781392	10	0.0194000	*
895.6025_10.452	rel_abund_log2	b1	b3	11	11	3.027163	10	0.0127000	*
895.6211_9.665	rel_abund_log2	b1	b3	11	11	-2.511628	10	0.0308000	*
897.6305_9.024	rel_abund_log2	b1	b3	11	11	4.061240	10	0.0022800	**
903.5878_9.033	rel_abund_log2	b1	b3	11	11	-2.266245	10	0.0469000	*
911.6459_9.666	rel_abund_log2	b1	b3	11	11	-4.719156	10	0.0008170	***
911.6461_9.397	rel_abund_log2	b1	b3	11	11	-3.955662	10	0.0027100	**
913.6162_9.035	rel_abund_log2	b1	b3	11	11	-2.708838	10	0.0220000	*
913.6613_10.438	rel_abund_log2	b1	b3	11	11	-2.360605	10	0.0399000	*
917.6482_10.589	rel_abund_log2	b1	b3	11	11	4.814375	10	0.0007080	***
918.5543_7.768	rel_abund_log2	b1	b3	11	11	3.677322	10	0.0042700	**
918.5546_7.77	rel_abund_log2	b1	b3	11	11	2.998605	10	0.0134000	*
919.6483_10.59	rel_abund_log2	b1	b3	11	11	2.885889	10	0.0162000	*
920.6735_10.31	rel_abund_log2	b1	b3	11	11	-5.455495	10	0.0002790	***
922.5774_7.695	rel_abund_log2	b1	b3	11	11	-2.624454	10	0.0254000	*
923.646_9.035	rel_abund_log2	b1	b3	11	11	-2.404971	10	0.0370000	*
923.6524_10.353	rel_abund_log2	b1	b3	11	11	-3.080048	10	0.0116000	*
924.518_7.766	rel_abund_log2	b1	b3	11	11	4.080322	10	0.0022100	**
925.6617_9.988	rel_abund_log2	b1	b3	11	11	-2.821525	10	0.0181000	*
927.677_10.59	rel_abund_log2	b1	b3	11	11	2.936365	10	0.0149000	*
930.5504_7.815	rel_abund_log2	b1	b3	11	11	-3.212695	10	0.0092900	**
931.5514_9.582	rel_abund_log2	b1	b3	11	11	2.553351	10	0.0287000	*
931.6162_9.992	rel_abund_log2	b1	b3	11	11	-2.624295	10	0.0254000	*
931.6162_9.999	rel_abund_log2	b1	b3	11	11	-2.662141	10	0.0238000	*
933.5855_8.781	rel_abund_log2	b1	b3	11	11	4.952301	10	0.0005770	***
934.5326_7.772	rel_abund_log2	b1	b3	11	11	3.165492	10	0.0101000	*
939.6769_10.335	rel_abund_log2	b1	b3	11	11	-3.036580	10	0.0125000	*
943.5073_8.524	rel_abund_log2	b1	b3	11	11	2.505690	10	0.0311000	*
959.6018_9.026	rel_abund_log2	b1	b3	11	11	2.797465	10	0.0189000	*
962.5631_8.939	rel_abund_log2	b1	b3	11	11	2.259713	10	0.0474000	*
966.5651_8.147	rel_abund_log2	b1	b3	11	11	2.354157	10	0.0404000	*
968.5707_8.019	rel_abund_log2	b1	b3	11	11	-4.221800	10	0.0017700	**
971.6542_9.988	rel_abund_log2	b1	b3	11	11	-2.726955	10	0.0213000	*
985.616_9.034	rel_abund_log2	b1	b3	11	11	-2.262055	10	0.0472000	*

# how many are significant?
nrow(ctrl_t.test_paired_sig)

## [1] 222

ANOVA overlap

Keep sig features in t-test that have a match in sig ANOVA

sig_overlap_ctrl_paired <- inner_join(ctrl_t.test_paired_sig,
                                   trt_tukeyHSD_sig,
                                   by = "mz_rt",
                                   suffix = c(".t-test", ".tukeys"))

Mummichog list

paired_ctrl_for_mummichog <- ctrl_t.test_paired %>%
  dplyr::select(mz_rt,
         p,
         statistic) %>%
  separate(col = mz_rt,
           into = c("m/z", "rt"),
           sep = "_") %>%
  rename("p-value" = "p") %>%
  rename("t-score" = "statistic")

write_csv(paired_ctrl_for_mummichog,
          "for mummichog analysis/t-test-res-ctrl-paired.csv")

Paired beta

# run paired t-tests for control intervention
beta_t.test_paired <- df_for_stats %>%
  filter(treatment == "beta") %>%
 dplyr::select(subject, period, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ period, 
         paired = TRUE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()

Statistically significant features

# which features are significant?
beta_t.test_paired_sig <- beta_t.test_paired %>%
  filter(p < 0.05)
kable(beta_t.test_paired_sig)

mz_rt	.y.	group1	group2	n1	n2	statistic	df	p	p.signif
117.0557_0.669	rel_abund_log2	b1	b3	12	12	2.934820	11	0.0136000	*
1235.798_10.088	rel_abund_log2	b1	b3	12	12	3.381989	11	0.0061200	**
1249.8137_10.468	rel_abund_log2	b1	b3	12	12	-2.783987	11	0.0178000	*
1279.8241_10.52	rel_abund_log2	b1	b3	12	12	3.378153	11	0.0061600	**
128.0352_0.617	rel_abund_log2	b1	b3	12	12	-2.332737	11	0.0397000	*
1334.2417_10.855	rel_abund_log2	b1	b3	12	12	2.566275	11	0.0262000	*
1335.245_10.854	rel_abund_log2	b1	b3	12	12	3.456240	11	0.0053700	**
1491.1091_6.544	rel_abund_log2	b1	b3	12	12	-2.972539	11	0.0127000	*
1554.1226_7.773	rel_abund_log2	b1	b3	12	12	2.358112	11	0.0379000	*
1558.1192_8.016	rel_abund_log2	b1	b3	12	12	-2.811807	11	0.0169000	*
1561.2028_9.038	rel_abund_log2	b1	b3	12	12	-3.619617	11	0.0040300	**
1598.0913_6.94	rel_abund_log2	b1	b3	12	12	-2.255567	11	0.0454000	*
1599.0952_6.94	rel_abund_log2	b1	b3	12	12	-2.283561	11	0.0433000	*
1602.1228_7.339	rel_abund_log2	b1	b3	12	12	-2.546669	11	0.0272000	*
1616.1136_7.768	rel_abund_log2	b1	b3	12	12	2.355819	11	0.0381000	*
1633.1422_7.775	rel_abund_log2	b1	b3	12	12	3.484757	11	0.0051100	**
1637.3241_10.442	rel_abund_log2	b1	b3	12	12	-3.336032	11	0.0066400	**
1641.1859_8.06	rel_abund_log2	b1	b3	12	12	-2.299035	11	0.0421000	*
1646.3475_10.439	rel_abund_log2	b1	b3	12	12	-2.612640	11	0.0241000	*
1655.1569_8.02	rel_abund_log2	b1	b3	12	12	-2.262519	11	0.0449000	*
1664.3521_10.59	rel_abund_log2	b1	b3	12	12	3.629224	11	0.0039600	**
1665.3553_10.589	rel_abund_log2	b1	b3	12	12	3.563350	11	0.0044500	**
1669.2155_8.46	rel_abund_log2	b1	b3	12	12	-2.495141	11	0.0298000	*
1670.349_9.987	rel_abund_log2	b1	b3	12	12	-2.643694	11	0.0228000	*
1674.3803_10.59	rel_abund_log2	b1	b3	12	12	3.221093	11	0.0081400	**
1675.384_10.589	rel_abund_log2	b1	b3	12	12	3.354842	11	0.0064200	**
172.9913_0.642	rel_abund_log2	b1	b3	12	12	3.157009	11	0.0091300	**
188.9862_0.644	rel_abund_log2	b1	b3	12	12	-3.262414	11	0.0075700	**
201.0226_0.688	rel_abund_log2	b1	b3	12	12	2.701885	11	0.0206000	*
201.0575_0.644	rel_abund_log2	b1	b3	12	12	2.444664	11	0.0326000	*
229.0537_0.703	rel_abund_log2	b1	b3	12	12	-2.809324	11	0.0170000	*
230.9967_0.644	rel_abund_log2	b1	b3	12	12	-3.087077	11	0.0103000	*
291.0839_0.618	rel_abund_log2	b1	b3	12	12	2.240244	11	0.0467000	*
352.0856_0.687	rel_abund_log2	b1	b3	12	12	-4.295763	11	0.0012600	**
362.9406_0.501	rel_abund_log2	b1	b3	12	12	-2.729799	11	0.0196000	*
397.2049_0.693	rel_abund_log2	b1	b3	12	12	2.627017	11	0.0235000	*
397.3681_5	rel_abund_log2	b1	b3	12	12	4.147661	11	0.0016200	**
411.3837_5.349	rel_abund_log2	b1	b3	12	12	6.602477	11	0.0000385	****
436.2827_3.522	rel_abund_log2	b1	b3	12	12	-3.789484	11	0.0030000	**
437.0542_0.688	rel_abund_log2	b1	b3	12	12	-3.234442	11	0.0079500	**
441.3942_4.718	rel_abund_log2	b1	b3	12	12	4.170438	11	0.0015600	**
462.1763_0.655	rel_abund_log2	b1	b3	12	12	-5.649381	11	0.0001490	***
464.3013_0.999	rel_abund_log2	b1	b3	12	12	2.762717	11	0.0185000	*
464.314_4.127	rel_abund_log2	b1	b3	12	12	-2.220324	11	0.0483000	*
467.3735_4.306	rel_abund_log2	b1	b3	12	12	2.651755	11	0.0225000	*
478.2933_3.354	rel_abund_log2	b1	b3	12	12	3.711591	11	0.0034300	**
506.3245_3.232	rel_abund_log2	b1	b3	12	12	2.381037	11	0.0364000	*
507.223_1.7	rel_abund_log2	b1	b3	12	12	-9.672208	11	0.0000010	****
507.223_2.273	rel_abund_log2	b1	b3	12	12	-7.745145	11	0.0000089	****
511.3997_4.453	rel_abund_log2	b1	b3	12	12	6.399569	11	0.0000508	****
514.2836_0.69	rel_abund_log2	b1	b3	12	12	2.934805	11	0.0136000	*
519.347_6.861	rel_abund_log2	b1	b3	12	12	2.627030	11	0.0235000	*
524.3351_3.376	rel_abund_log2	b1	b3	12	12	-2.633968	11	0.0232000	*
526.3144_2.683	rel_abund_log2	b1	b3	12	12	-3.371109	11	0.0062400	**
528.2631_0.691	rel_abund_log2	b1	b3	12	12	2.440700	11	0.0328000	*
539.4309_4.416	rel_abund_log2	b1	b3	12	12	2.407874	11	0.0347000	*
546.1884_6.859	rel_abund_log2	b1	b3	12	12	-2.293405	11	0.0425000	*
551.3582_4.841	rel_abund_log2	b1	b3	12	12	-9.410498	11	0.0000014	****
554.3456_3.441	rel_abund_log2	b1	b3	12	12	-3.300291	11	0.0070700	**
558.3148_3.234	rel_abund_log2	b1	b3	12	12	3.113373	11	0.0098700	**
559.4719_6.867	rel_abund_log2	b1	b3	12	12	4.712253	11	0.0006370	***
566.3462_3.232	rel_abund_log2	b1	b3	12	12	3.036030	11	0.0113000	*
567.4621_4.643	rel_abund_log2	b1	b3	12	12	4.257497	11	0.0013500	**
573.4515_4.266	rel_abund_log2	b1	b3	12	12	5.282792	11	0.0002590	***
575.4672_5.354	rel_abund_log2	b1	b3	12	12	4.902049	11	0.0004700	***
575.467_4.662	rel_abund_log2	b1	b3	12	12	5.473259	11	0.0001940	***
577.3738_4.918	rel_abund_log2	b1	b3	12	12	-12.254423	11	0.0000001	****
579.3893_5.557	rel_abund_log2	b1	b3	12	12	-10.034969	11	0.0000007	****
591.3894_5.174	rel_abund_log2	b1	b3	12	12	-5.435694	11	0.0002050	***
591.462_4.314	rel_abund_log2	b1	b3	12	12	2.390997	11	0.0358000	*
591.462_4.651	rel_abund_log2	b1	b3	12	12	4.210972	11	0.0014600	**
593.4778_4.664	rel_abund_log2	b1	b3	12	12	5.258091	11	0.0002690	***
594.3768_3.858	rel_abund_log2	b1	b3	12	12	2.578956	11	0.0256000	*
595.4933_4.953	rel_abund_log2	b1	b3	12	12	13.598942	11	0.0000000	****
605.1555_0.616	rel_abund_log2	b1	b3	12	12	2.816816	11	0.0168000	*
618.308_3.232	rel_abund_log2	b1	b3	12	12	2.254115	11	0.0456000	*
624.3379_0.689	rel_abund_log2	b1	b3	12	12	-2.882873	11	0.0149000	*
634.3329_3.232	rel_abund_log2	b1	b3	12	12	2.857638	11	0.0156000	*
640.5796_10.729	rel_abund_log2	b1	b3	12	12	-2.639749	11	0.0230000	*
654.5589_10.54	rel_abund_log2	b1	b3	12	12	2.259625	11	0.0451000	*
656.575_10.694	rel_abund_log2	b1	b3	12	12	3.001030	11	0.0121000	*
664.5877_10.541	rel_abund_log2	b1	b3	12	12	3.033159	11	0.0114000	*
668.5747_10.647	rel_abund_log2	b1	b3	12	12	-2.812232	11	0.0169000	*
670.5743_10.646	rel_abund_log2	b1	b3	12	12	-2.404081	11	0.0350000	*
674.6039_10.834	rel_abund_log2	b1	b3	12	12	-5.534071	11	0.0001770	***
676.6243_10.731	rel_abund_log2	b1	b3	12	12	-4.813784	11	0.0005410	***
678.6034_10.645	rel_abund_log2	b1	b3	12	12	-2.592950	11	0.0250000	*
678.6402_10.885	rel_abund_log2	b1	b3	12	12	-3.832092	11	0.0027800	**
682.6332_10.835	rel_abund_log2	b1	b3	12	12	-3.893687	11	0.0025000	**
684.6065_10.853	rel_abund_log2	b1	b3	12	12	3.331193	11	0.0067000	**
696.3033_3.233	rel_abund_log2	b1	b3	12	12	2.674102	11	0.0216000	*
696.6064_10.749	rel_abund_log2	b1	b3	12	12	-4.484943	11	0.0009240	***
698.3189_3.775	rel_abund_log2	b1	b3	12	12	-3.224906	11	0.0080900	**
698.5121_7.962	rel_abund_log2	b1	b3	12	12	-3.544746	11	0.0045900	**
698.6108_10.776	rel_abund_log2	b1	b3	12	12	-2.904987	11	0.0143000	*
698.6217_10.912	rel_abund_log2	b1	b3	12	12	-2.861408	11	0.0155000	*
700.6211_10.914	rel_abund_log2	b1	b3	12	12	-3.433943	11	0.0055800	**
705.5178_5.308	rel_abund_log2	b1	b3	12	12	-3.494284	11	0.0050200	**
716.5225_8.139	rel_abund_log2	b1	b3	12	12	2.878249	11	0.0150000	*
717.5179_5.106	rel_abund_log2	b1	b3	12	12	-3.753869	11	0.0031900	**
721.549_6	rel_abund_log2	b1	b3	12	12	-4.722297	11	0.0006270	***
722.5123_7.861	rel_abund_log2	b1	b3	12	12	-3.082196	11	0.0104000	*
724.5276_8.3	rel_abund_log2	b1	b3	12	12	-4.621077	11	0.0007390	***
724.8722_0.515	rel_abund_log2	b1	b3	12	12	-2.377499	11	0.0367000	*
725.6412_10.916	rel_abund_log2	b1	b3	12	12	-2.422712	11	0.0338000	*
726.5434_9.157	rel_abund_log2	b1	b3	12	12	-2.271749	11	0.0442000	*
740.5225_7.515	rel_abund_log2	b1	b3	12	12	2.294404	11	0.0424000	*
744.5538_9.321	rel_abund_log2	b1	b3	12	12	4.235466	11	0.0014000	**
746.5966_10.853	rel_abund_log2	b1	b3	12	12	4.712025	11	0.0006380	***
750.5436_9.042	rel_abund_log2	b1	b3	12	12	-3.482944	11	0.0051200	**
752.5591_9.514	rel_abund_log2	b1	b3	12	12	-2.844641	11	0.0159000	*
759.5646_6.287	rel_abund_log2	b1	b3	12	12	-3.042100	11	0.0112000	*
761.5803_7.053	rel_abund_log2	b1	b3	12	12	-2.873554	11	0.0151000	*
762.5643_8.206	rel_abund_log2	b1	b3	12	12	-2.651552	11	0.0225000	*
762.6216_10.854	rel_abund_log2	b1	b3	12	12	2.290455	11	0.0427000	*
764.5798_8.343	rel_abund_log2	b1	b3	12	12	-2.417121	11	0.0342000	*
765.5735_6.098	rel_abund_log2	b1	b3	12	12	-2.476697	11	0.0308000	*
771.5646_6.096	rel_abund_log2	b1	b3	12	12	-2.518160	11	0.0286000	*
776.5351_7.576	rel_abund_log2	b1	b3	12	12	-3.096590	11	0.0102000	*
776.5354_7.574	rel_abund_log2	b1	b3	12	12	-2.943227	11	0.0134000	*
777.6118_7.986	rel_abund_log2	b1	b3	12	12	-2.867323	11	0.0153000	*
778.5745_10.284	rel_abund_log2	b1	b3	12	12	-2.625289	11	0.0236000	*
780.4705_7.867	rel_abund_log2	b1	b3	12	12	-3.918873	11	0.0024000	**
786.5646_7.575	rel_abund_log2	b1	b3	12	12	-3.707665	11	0.0034600	**
788.5437_6.549	rel_abund_log2	b1	b3	12	12	-2.376981	11	0.0367000	*
789.6114_7.911	rel_abund_log2	b1	b3	12	12	-3.829449	11	0.0028000	**
789.6116_8.177	rel_abund_log2	b1	b3	12	12	-3.565200	11	0.0044300	**
790.4993_7.863	rel_abund_log2	b1	b3	12	12	-3.524980	11	0.0047600	**
790.5953_8.511	rel_abund_log2	b1	b3	12	12	3.029372	11	0.0115000	*
792.8596_0.515	rel_abund_log2	b1	b3	12	12	-2.512410	11	0.0289000	*
793.5985_8.765	rel_abund_log2	b1	b3	12	12	2.687496	11	0.0211000	*
800.5357_7.469	rel_abund_log2	b1	b3	12	12	-3.465914	11	0.0052800	**
801.5445_6.55	rel_abund_log2	b1	b3	12	12	-2.868405	11	0.0153000	*
803.6275_8.771	rel_abund_log2	b1	b3	12	12	2.664602	11	0.0220000	*
804.5762_7.774	rel_abund_log2	b1	b3	12	12	2.657796	11	0.0223000	*
804.6308_8.768	rel_abund_log2	b1	b3	12	12	2.894348	11	0.0146000	*
805.6426_9.222	rel_abund_log2	b1	b3	12	12	-2.310559	11	0.0413000	*
806.6057_10.613	rel_abund_log2	b1	b3	12	12	-2.264047	11	0.0448000	*
808.5018_9.051	rel_abund_log2	b1	b3	12	12	-3.982237	11	0.0021500	**
810.5647_7.467	rel_abund_log2	b1	b3	12	12	-4.545558	11	0.0008360	***
812.5436_6.468	rel_abund_log2	b1	b3	12	12	-3.950371	11	0.0022700	**
812.58_7.898	rel_abund_log2	b1	b3	12	12	-3.993280	11	0.0021100	**
815.627_8.417	rel_abund_log2	b1	b3	12	12	-2.404808	11	0.0349000	*
816.5308_6.915	rel_abund_log2	b1	b3	12	12	-2.337500	11	0.0393000	*
816.5751_7.553	rel_abund_log2	b1	b3	12	12	-2.587367	11	0.0253000	*
818.5462_7.192	rel_abund_log2	b1	b3	12	12	3.590115	11	0.0042400	**
818.5462_7.337	rel_abund_log2	b1	b3	12	12	-3.160285	11	0.0090700	**
818.6271_10.414	rel_abund_log2	b1	b3	12	12	5.399015	11	0.0002170	***
819.6141_8.816	rel_abund_log2	b1	b3	12	12	-5.841126	11	0.0001120	***
819.6141_9.024	rel_abund_log2	b1	b3	12	12	3.914524	11	0.0024200	**
820.5155_3.079	rel_abund_log2	b1	b3	12	12	-2.886397	11	0.0148000	*
820.5462_7.34	rel_abund_log2	b1	b3	12	12	-2.296972	11	0.0423000	*
820.5463_7.196	rel_abund_log2	b1	b3	12	12	2.257837	11	0.0453000	*
820.627_10.414	rel_abund_log2	b1	b3	12	12	3.933180	11	0.0023400	**
821.6139_9.026	rel_abund_log2	b1	b3	12	12	4.331717	11	0.0011900	**
822.5774_8.941	rel_abund_log2	b1	b3	12	12	2.675168	11	0.0216000	*
823.6294_9.979	rel_abund_log2	b1	b3	12	12	4.692842	11	0.0006580	***
824.5433_5.925	rel_abund_log2	b1	b3	12	12	-2.818684	11	0.0167000	*
824.5771_8.938	rel_abund_log2	b1	b3	12	12	3.480650	11	0.0051400	**
824.5789_7.841	rel_abund_log2	b1	b3	12	12	-4.800002	11	0.0005530	***
826.5606_6.953	rel_abund_log2	b1	b3	12	12	-2.320523	11	0.0405000	*
827.6272_7.905	rel_abund_log2	b1	b3	12	12	-3.811589	11	0.0028800	**
828.5668_8.612	rel_abund_log2	b1	b3	12	12	-4.366137	11	0.0011200	**
828.5759_7.19	rel_abund_log2	b1	b3	12	12	3.219035	11	0.0081700	**
828.5759_7.339	rel_abund_log2	b1	b3	12	12	-2.906686	11	0.0143000	*
828.6559_10.414	rel_abund_log2	b1	b3	12	12	3.170122	11	0.0089200	**
829.6431_8.816	rel_abund_log2	b1	b3	12	12	-2.927992	11	0.0137000	*
829.6432_9.024	rel_abund_log2	b1	b3	12	12	4.504711	11	0.0008940	***
830.5815_7.188	rel_abund_log2	b1	b3	12	12	2.847985	11	0.0159000	*
831.6576_9.28	rel_abund_log2	b1	b3	12	12	-4.136092	11	0.0016500	**
831.659_9.98	rel_abund_log2	b1	b3	12	12	3.910747	11	0.0024300	**
832.6067_8.94	rel_abund_log2	b1	b3	12	12	2.813332	11	0.0169000	*
832.6428_10.551	rel_abund_log2	b1	b3	12	12	-2.972107	11	0.0127000	*
833.5544_7.557	rel_abund_log2	b1	b3	12	12	-3.563603	11	0.0044400	**
833.6294_9.395	rel_abund_log2	b1	b3	12	12	-3.704948	11	0.0034700	**
834.5643_7.242	rel_abund_log2	b1	b3	12	12	-2.484852	11	0.0303000	*
835.5322_8.336	rel_abund_log2	b1	b3	12	12	4.707655	11	0.0006420	***
835.6457_10.444	rel_abund_log2	b1	b3	12	12	-2.471447	11	0.0310000	*
836.5434_6.295	rel_abund_log2	b1	b3	12	12	-2.757764	11	0.0186000	*
837.6571_10.552	rel_abund_log2	b1	b3	12	12	-2.780186	11	0.0179000	*
838.5956_8.612	rel_abund_log2	b1	b3	12	12	-2.656729	11	0.0223000	*
840.5304_6.407	rel_abund_log2	b1	b3	12	12	-3.822264	11	0.0028300	**
840.5749_7.321	rel_abund_log2	b1	b3	12	12	-3.340764	11	0.0065800	**
840.5749_7.471	rel_abund_log2	b1	b3	12	12	-4.627852	11	0.0007310	***
841.5661_6.75	rel_abund_log2	b1	b3	12	12	-2.719296	11	0.0200000	*
841.6406_8.452	rel_abund_log2	b1	b3	12	12	-4.113938	11	0.0017200	**
842.5154_6.099	rel_abund_log2	b1	b3	12	12	-2.684742	11	0.0212000	*
842.5905_7.891	rel_abund_log2	b1	b3	12	12	-4.641280	11	0.0007150	***
843.6586_9.396	rel_abund_log2	b1	b3	12	12	-4.736375	11	0.0006130	***
845.5636_7.343	rel_abund_log2	b1	b3	12	12	-3.440275	11	0.0055200	**
845.6746_10.439	rel_abund_log2	b1	b3	12	12	-2.292469	11	0.0426000	*
846.6588_10.643	rel_abund_log2	b1	b3	12	12	3.546346	11	0.0045800	**
847.6304_9.032	rel_abund_log2	b1	b3	12	12	-2.535210	11	0.0277000	*
847.6455_10.298	rel_abund_log2	b1	b3	12	12	4.288894	11	0.0012800	**
848.5114_9.046	rel_abund_log2	b1	b3	12	12	-3.722530	11	0.0033700	**
848.577_8.458	rel_abund_log2	b1	b3	12	12	-2.528468	11	0.0280000	*
849.6462_9.988	rel_abund_log2	b1	b3	12	12	-3.677868	11	0.0036400	**
849.6616_10.589	rel_abund_log2	b1	b3	12	12	4.277192	11	0.0013000	**
850.5595_6.407	rel_abund_log2	b1	b3	12	12	-4.652950	11	0.0007020	***
851.6615_10.59	rel_abund_log2	b1	b3	12	12	2.948328	11	0.0132000	*
853.619_10.659	rel_abund_log2	b1	b3	12	12	-4.213208	11	0.0014500	**
853.6428_8.12	rel_abund_log2	b1	b3	12	12	-5.388563	11	0.0002200	***
854.5519_7.576	rel_abund_log2	b1	b3	12	12	-4.247337	11	0.0013700	**
854.5906_7.701	rel_abund_log2	b1	b3	12	12	-3.798355	11	0.0029500	**
854.5918_8.021	rel_abund_log2	b1	b3	12	12	-2.961798	11	0.0129000	*
855.59_8.755	rel_abund_log2	b1	b3	12	12	2.636432	11	0.0231000	*
855.6591_9.033	rel_abund_log2	b1	b3	12	12	-3.069368	11	0.0107000	*
856.6051_8.21	rel_abund_log2	b1	b3	12	12	2.387966	11	0.0360000	*
856.6871_10.641	rel_abund_log2	b1	b3	12	12	4.595975	11	0.0007700	***
857.6737_9.513	rel_abund_log2	b1	b3	12	12	-9.480190	11	0.0000013	****
857.6746_10.294	rel_abund_log2	b1	b3	12	12	3.673240	11	0.0036700	**
857.6749_9.986	rel_abund_log2	b1	b3	12	12	-3.130171	11	0.0095700	**
859.5338_7.864	rel_abund_log2	b1	b3	12	12	-2.642053	11	0.0229000	*
859.6907_10.59	rel_abund_log2	b1	b3	12	12	3.673498	11	0.0036700	**
860.6369_10.19	rel_abund_log2	b1	b3	12	12	4.291580	11	0.0012700	**
860.6919_10.384	rel_abund_log2	b1	b3	12	12	-5.527332	11	0.0001790	***
862.5261_7.47	rel_abund_log2	b1	b3	12	12	-3.872158	11	0.0026000	**
862.527_7.471	rel_abund_log2	b1	b3	12	12	-3.919768	11	0.0023900	**
862.6535_10.583	rel_abund_log2	b1	b3	12	12	2.224550	11	0.0480000	*
862.664_10.445	rel_abund_log2	b1	b3	12	12	-2.523894	11	0.0283000	*
863.5644_9.582	rel_abund_log2	b1	b3	12	12	9.018235	11	0.0000021	****
863.6765_10.683	rel_abund_log2	b1	b3	12	12	-4.585805	11	0.0007830	***
864.5747_7.263	rel_abund_log2	b1	b3	12	12	-2.456400	11	0.0319000	*
865.6758_10.678	rel_abund_log2	b1	b3	12	12	-2.778335	11	0.0180000	*
866.6268_8.879	rel_abund_log2	b1	b3	12	12	2.714971	11	0.0201000	*
868.5303_7.476	rel_abund_log2	b1	b3	12	12	-2.594796	11	0.0249000	*
870.5618_7.814	rel_abund_log2	b1	b3	12	12	-2.787781	11	0.0177000	*
871.5805_8.017	rel_abund_log2	b1	b3	12	12	-3.059198	11	0.0109000	*
871.6147_8.768	rel_abund_log2	b1	b3	12	12	2.383383	11	0.0363000	*
871.6891_10.337	rel_abund_log2	b1	b3	12	12	-6.286003	11	0.0000595	****
872.5626_7.769	rel_abund_log2	b1	b3	12	12	2.846966	11	0.0159000	*
872.6815_10.583	rel_abund_log2	b1	b3	12	12	2.762710	11	0.0185000	*
874.559_6.259	rel_abund_log2	b1	b3	12	12	-2.845618	11	0.0159000	*
874.6643_9.99	rel_abund_log2	b1	b3	12	12	-2.701647	11	0.0206000	*
876.5748_6.674	rel_abund_log2	b1	b3	12	12	-2.416498	11	0.0342000	*
878.584_5.681	rel_abund_log2	b1	b3	12	12	-2.518898	11	0.0285000	*
878.5903_7.305	rel_abund_log2	b1	b3	12	12	-5.052135	11	0.0003710	***
880.5375_7.198	rel_abund_log2	b1	b3	12	12	3.564089	11	0.0044400	**
880.6062_8.517	rel_abund_log2	b1	b3	12	12	-2.745637	11	0.0190000	*
881.5397_7.202	rel_abund_log2	b1	b3	12	12	3.452148	11	0.0054100	**
881.6055_9.023	rel_abund_log2	b1	b3	12	12	3.594388	11	0.0042100	**
882.6209_8.855	rel_abund_log2	b1	b3	12	12	-2.591754	11	0.0251000	*
883.5331_7.753	rel_abund_log2	b1	b3	12	12	-2.973553	11	0.0127000	*
883.6211_9.976	rel_abund_log2	b1	b3	12	12	3.979468	11	0.0021600	**
884.5587_7.554	rel_abund_log2	b1	b3	12	12	-2.240891	11	0.0466000	*
884.5687_8.94	rel_abund_log2	b1	b3	12	12	3.732280	11	0.0033100	**
886.5195_6.961	rel_abund_log2	b1	b3	12	12	2.258508	11	0.0452000	*
886.5331_7.335	rel_abund_log2	b1	b3	12	12	-2.809623	11	0.0170000	*
887.5991_9.03	rel_abund_log2	b1	b3	12	12	7.018997	11	0.0000221	****
887.599_8.816	rel_abund_log2	b1	b3	12	12	-2.525994	11	0.0282000	*
889.617_9.974	rel_abund_log2	b1	b3	12	12	3.263960	11	0.0075500	**
890.5649_8.939	rel_abund_log2	b1	b3	12	12	3.170605	11	0.0089100	**
896.5624_7.191	rel_abund_log2	b1	b3	12	12	2.864182	11	0.0154000	*
896.6737_10.623	rel_abund_log2	b1	b3	12	12	3.612493	11	0.0040800	**
897.6305_9.024	rel_abund_log2	b1	b3	12	12	3.528349	11	0.0047300	**
904.6171_10.646	rel_abund_log2	b1	b3	12	12	2.787425	11	0.0177000	*
906.5531_8.016	rel_abund_log2	b1	b3	12	12	-2.208504	11	0.0493000	*
909.6366_10.295	rel_abund_log2	b1	b3	12	12	3.494400	11	0.0050200	**
909.6368_9.99	rel_abund_log2	b1	b3	12	12	-3.158108	11	0.0091100	**
911.5645_8.686	rel_abund_log2	b1	b3	12	12	-2.330918	11	0.0398000	*
911.6461_9.397	rel_abund_log2	b1	b3	12	12	-2.856518	11	0.0156000	*
913.6162_9.035	rel_abund_log2	b1	b3	12	12	-2.934788	11	0.0136000	*
913.6613_10.438	rel_abund_log2	b1	b3	12	12	-2.251360	11	0.0458000	*
914.5643_8.457	rel_abund_log2	b1	b3	12	12	-2.333356	11	0.0396000	*
915.6319_9.987	rel_abund_log2	b1	b3	12	12	-2.421769	11	0.0339000	*
915.6327_10.293	rel_abund_log2	b1	b3	12	12	2.703322	11	0.0205000	*
917.5422_7.049	rel_abund_log2	b1	b3	12	12	2.860671	11	0.0155000	*
917.6329_9.989	rel_abund_log2	b1	b3	12	12	-3.975171	11	0.0021800	**
917.633_10.292	rel_abund_log2	b1	b3	12	12	2.658804	11	0.0222000	*
917.6482_10.589	rel_abund_log2	b1	b3	12	12	2.600068	11	0.0247000	*
918.5466_6.408	rel_abund_log2	b1	b3	12	12	-2.353783	11	0.0382000	*
919.2277_0.616	rel_abund_log2	b1	b3	12	12	2.835237	11	0.0162000	*
920.6735_10.31	rel_abund_log2	b1	b3	12	12	-3.161928	11	0.0090500	**
922.5024_7.08	rel_abund_log2	b1	b3	12	12	2.346660	11	0.0387000	*
922.5774_7.695	rel_abund_log2	b1	b3	12	12	-2.605611	11	0.0244000	*
923.646_9.035	rel_abund_log2	b1	b3	12	12	-2.811329	11	0.0169000	*
923.6524_10.353	rel_abund_log2	b1	b3	12	12	-4.250402	11	0.0013600	**
924.5935_8.459	rel_abund_log2	b1	b3	12	12	-2.831952	11	0.0163000	*
925.6617_9.988	rel_abund_log2	b1	b3	12	12	-3.413678	11	0.0057900	**
927.677_10.59	rel_abund_log2	b1	b3	12	12	3.768684	11	0.0031100	**
93.0344_0.646	rel_abund_log2	b1	b3	12	12	2.767441	11	0.0183000	*
931.5514_9.582	rel_abund_log2	b1	b3	12	12	6.307733	11	0.0000577	****
931.6162_9.992	rel_abund_log2	b1	b3	12	12	-3.439948	11	0.0055300	**
931.6162_9.999	rel_abund_log2	b1	b3	12	12	-3.523352	11	0.0047700	**
933.5855_8.781	rel_abund_log2	b1	b3	12	12	2.206123	11	0.0496000	*
934.5326_7.772	rel_abund_log2	b1	b3	12	12	2.827829	11	0.0164000	*
939.6769_10.335	rel_abund_log2	b1	b3	12	12	-5.349194	11	0.0002340	***
940.5496_7.764	rel_abund_log2	b1	b3	12	12	2.603873	11	0.0245000	*
942.5548_7.339	rel_abund_log2	b1	b3	12	12	-3.096144	11	0.0102000	*
945.6385_9.975	rel_abund_log2	b1	b3	12	12	5.023253	11	0.0003880	***
948.5172_7.192	rel_abund_log2	b1	b3	12	12	2.211315	11	0.0491000	*
950.6082_8.859	rel_abund_log2	b1	b3	12	12	-3.697007	11	0.0035200	**
958.5324_7.191	rel_abund_log2	b1	b3	12	12	4.769190	11	0.0005810	***
959.6018_9.026	rel_abund_log2	b1	b3	12	12	3.011016	11	0.0118000	*
960.7412_11.038	rel_abund_log2	b1	b3	12	12	-5.355291	11	0.0002320	***
961.6178_9.977	rel_abund_log2	b1	b3	12	12	2.863790	11	0.0154000	*
967.6322_9.976	rel_abund_log2	b1	b3	12	12	3.507409	11	0.0049100	**
968.581_8.937	rel_abund_log2	b1	b3	12	12	2.760953	11	0.0185000	*
969.6384_9.033	rel_abund_log2	b1	b3	12	12	-2.966128	11	0.0128000	*
970.5858_8.459	rel_abund_log2	b1	b3	12	12	-2.377833	11	0.0366000	*
970.6153_10.616	rel_abund_log2	b1	b3	12	12	-3.245496	11	0.0078000	**
971.6542_9.988	rel_abund_log2	b1	b3	12	12	-2.893852	11	0.0146000	*
976.5477_8.457	rel_abund_log2	b1	b3	12	12	-2.850174	11	0.0158000	*
983.6184_9.976	rel_abund_log2	b1	b3	12	12	-3.674388	11	0.0036600	**
983.6185_9.993	rel_abund_log2	b1	b3	12	12	-2.741676	11	0.0192000	*
985.616_9.034	rel_abund_log2	b1	b3	12	12	-2.202084	11	0.0499000	*
986.5637_8.459	rel_abund_log2	b1	b3	12	12	-4.993769	11	0.0004070	***
987.6318_9.987	rel_abund_log2	b1	b3	12	12	-3.215418	11	0.0082300	**
990.5651_8.013	rel_abund_log2	b1	b3	12	12	-2.503305	11	0.0293000	*
992.5806_8.459	rel_abund_log2	b1	b3	12	12	-4.293004	11	0.0012700	**
993.6488_9.986	rel_abund_log2	b1	b3	12	12	-2.603555	11	0.0245000	*

# how many are significant?
nrow(beta_t.test_paired_sig)

## [1] 311

Remove BG diet

# let's grab both datasets for features sig in pre v post beta and pre v post control and combine them
fulljoin_t.test_betaANDctrl <- full_join(beta_t.test_paired_sig, ctrl_t.test_paired_sig,
                                        by = "mz_rt",
                                        suffix = c(".beta", ".ctrl"))

# number of features in combined list
nrow(fulljoin_t.test_betaANDctrl)

## [1] 389

Let’s try and remove features significant due to the background diet

sig_paired_beta_rmBG <- fulljoin_t.test_betaANDctrl %>%
  # add a column to account for direction
  mutate(sign = statistic.beta * statistic.ctrl) %>%
  # replace NAs in the sign column with 0
  mutate(sign = replace_na(sign, 0)) %>%
  # replace NAs in the statistic.ctrl column to 0
  mutate(statistic.ctrl = replace_na(statistic.ctrl, 0)) %>%
  # filter for columns that are either negative (means ctrl and tomato are going in opposite dir) or where the stat.ctrl col is 0 (so we don't remove features that are just not present in the sig control list)
  filter((sign < 0 | statistic.ctrl == 0))

# number of features without bg diet effect
nrow(sig_paired_beta_rmBG)

## [1] 167

How many BG-diet-related features did we remove from the list of significant beta features?

nrow(beta_t.test_paired_sig) - nrow(sig_paired_beta_rmBG)

## [1] 144

ANOVA overlap

Keep sig features in t-test that have a match in sig ANOVA. Let’s take our new feature list (background diet effect removed)

# select only features from paired list that have a match in ANOVA list
beta_sig_ANOVA_overlap_paired <- inner_join(sig_paired_beta_rmBG,
                                            trt_tukeyHSD_sig,
                                            by = "mz_rt",
                                            suffix = c(".t-test", ".tukeys"))

# features overlapping with sig ANOVA
unique(beta_sig_ANOVA_overlap_paired$mz_rt)

##  [1] "1561.2028_9.038" "526.3144_2.683"  "674.6039_10.834" "682.6332_10.835"
##  [5] "705.5178_5.308"  "721.549_6"       "764.5798_8.343"  "800.5357_7.469" 
##  [9] "824.5771_8.938"  "847.6455_10.298" "848.5114_9.046"  "853.619_10.659" 
## [13] "857.6749_9.986"  "863.5644_9.582"  "868.5303_7.476"  "887.5991_9.03"  
## [17] "909.6368_9.99"   "915.6327_10.293" "917.6329_9.989"  "940.5496_7.764" 
## [21] "945.6385_9.975"  "969.6384_9.033"  "983.6184_9.976"  "983.6185_9.993" 
## [25] "987.6318_9.987"

Mummichog list

Pulling features that are significant in paired t-test due to treatment, not due to background diet.

paired_beta_for_mummichog <- sig_paired_beta_rmBG %>%
  dplyr::select(mz_rt,
         p.beta,
         statistic.beta) %>%
  separate(col = mz_rt,
           into = c("m/z", "rt"),
           sep = "_") %>%
  rename("p-value" = "p.beta") %>%
  rename("t-score" = "statistic.beta")

write_csv(paired_beta_for_mummichog,
          "for mummichog analysis/t-test-res-beta-paired.csv")

Paired red

# run paired t-tests for control intervention
red_t.test_paired <- df_for_stats %>%
  filter(treatment == "red") %>%
 dplyr::select(subject, period, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ period, 
         paired = TRUE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()

Statistically significant features

# which features are significant?
red_t.test_paired_sig <- red_t.test_paired %>%
  filter(p < 0.05)
kable(red_t.test_paired_sig)

mz_rt	.y.	group1	group2	n1	n2	statistic	df	p	p.signif
1002.5196_7.769	rel_abund_log2	b1	b3	12	12	5.087474	11	0.0003510	***
1008.5435_7.813	rel_abund_log2	b1	b3	12	12	-2.484718	11	0.0303000	*
1061.6776_3.089	rel_abund_log2	b1	b3	12	12	2.255507	11	0.0454000	*
1135.6242_2.73	rel_abund_log2	b1	b3	12	12	2.839018	11	0.0161000	*
117.0557_0.669	rel_abund_log2	b1	b3	12	12	3.518094	11	0.0048100	**
1279.8241_10.52	rel_abund_log2	b1	b3	12	12	2.411636	11	0.0345000	*
1334.2417_10.855	rel_abund_log2	b1	b3	12	12	2.677224	11	0.0215000	*
1335.245_10.854	rel_abund_log2	b1	b3	12	12	2.512060	11	0.0289000	*
1554.1226_7.773	rel_abund_log2	b1	b3	12	12	3.891711	11	0.0025100	**
1555.1258_7.773	rel_abund_log2	b1	b3	12	12	2.649203	11	0.0226000	*
1561.2028_9.038	rel_abund_log2	b1	b3	12	12	-4.406847	11	0.0010500	**
1579.1408_6.75	rel_abund_log2	b1	b3	12	12	-2.328366	11	0.0400000	*
1600.0951_6.94	rel_abund_log2	b1	b3	12	12	-2.280807	11	0.0435000	*
1616.1136_7.768	rel_abund_log2	b1	b3	12	12	3.714983	11	0.0034100	**
1632.139_7.776	rel_abund_log2	b1	b3	12	12	3.115819	11	0.0098200	**
1633.1422_7.775	rel_abund_log2	b1	b3	12	12	2.862239	11	0.0155000	*
1654.1535_8.02	rel_abund_log2	b1	b3	12	12	-3.315537	11	0.0068900	**
1655.1569_8.02	rel_abund_log2	b1	b3	12	12	-2.281514	11	0.0434000	*
1666.3553_10.589	rel_abund_log2	b1	b3	12	12	2.272183	11	0.0441000	*
201.0226_0.688	rel_abund_log2	b1	b3	12	12	3.106809	11	0.0099800	**
213.0223_0.689	rel_abund_log2	b1	b3	12	12	-5.881212	11	0.0001060	***
231.0795_0.662	rel_abund_log2	b1	b3	12	12	2.314161	11	0.0410000	*
239.0923_0.975	rel_abund_log2	b1	b3	12	12	4.170083	11	0.0015600	**
279.2327_4.208	rel_abund_log2	b1	b3	12	12	-2.327239	11	0.0401000	*
311.2224_2.465	rel_abund_log2	b1	b3	12	12	3.592636	11	0.0042200	**
316.9477_0.518	rel_abund_log2	b1	b3	12	12	-2.644557	11	0.0228000	*
343.9948_0.639	rel_abund_log2	b1	b3	12	12	-2.386287	11	0.0361000	*
352.0856_0.687	rel_abund_log2	b1	b3	12	12	-3.710290	11	0.0034400	**
397.3681_5	rel_abund_log2	b1	b3	12	12	2.343181	11	0.0390000	*
411.3837_5.349	rel_abund_log2	b1	b3	12	12	4.978168	11	0.0004170	***
413.1998_0.688	rel_abund_log2	b1	b3	12	12	2.742834	11	0.0191000	*
437.0542_0.688	rel_abund_log2	b1	b3	12	12	-2.526373	11	0.0282000	*
441.3942_4.718	rel_abund_log2	b1	b3	12	12	3.948416	11	0.0022800	**
444.0682_4.208	rel_abund_log2	b1	b3	12	12	-2.387538	11	0.0360000	*
447.3471_4.631	rel_abund_log2	b1	b3	12	12	2.917431	11	0.0140000	*
462.1763_0.655	rel_abund_log2	b1	b3	12	12	-3.921660	11	0.0023900	**
478.2933_3.354	rel_abund_log2	b1	b3	12	12	3.510513	11	0.0048800	**
491.3733_4.784	rel_abund_log2	b1	b3	12	12	2.573233	11	0.0259000	*
493.3349_5.699	rel_abund_log2	b1	b3	12	12	-2.437924	11	0.0329000	*
504.3089_2.726	rel_abund_log2	b1	b3	12	12	2.726291	11	0.0197000	*
506.3245_3.232	rel_abund_log2	b1	b3	12	12	2.764072	11	0.0184000	*
507.223_1.7	rel_abund_log2	b1	b3	12	12	-6.642237	11	0.0000365	****
507.223_2.273	rel_abund_log2	b1	b3	12	12	-5.376197	11	0.0002250	***
538.3144_2.488	rel_abund_log2	b1	b3	12	12	2.752197	11	0.0188000	*
539.4309_4.416	rel_abund_log2	b1	b3	12	12	2.738945	11	0.0193000	*
551.3582_4.841	rel_abund_log2	b1	b3	12	12	-5.655791	11	0.0001480	***
557.4566_6.309	rel_abund_log2	b1	b3	12	12	4.960029	11	0.0004290	***
557.457_6.865	rel_abund_log2	b1	b3	12	12	5.928513	11	0.0000989	****
558.3148_3.234	rel_abund_log2	b1	b3	12	12	2.722424	11	0.0198000	*
559.4719_6.867	rel_abund_log2	b1	b3	12	12	4.504786	11	0.0008940	***
561.2272_6.889	rel_abund_log2	b1	b3	12	12	4.524246	11	0.0008660	***
566.3462_3.232	rel_abund_log2	b1	b3	12	12	3.698232	11	0.0035100	**
567.4621_4.643	rel_abund_log2	b1	b3	12	12	4.258499	11	0.0013500	**
573.4515_4.266	rel_abund_log2	b1	b3	12	12	3.883000	11	0.0025500	**
573.4515_4.873	rel_abund_log2	b1	b3	12	12	4.950609	11	0.0004350	***
575.4672_5.354	rel_abund_log2	b1	b3	12	12	5.966417	11	0.0000937	****
575.467_4.662	rel_abund_log2	b1	b3	12	12	4.324484	11	0.0012100	**
577.3738_4.918	rel_abund_log2	b1	b3	12	12	-4.802641	11	0.0005510	***
579.3893_5.557	rel_abund_log2	b1	b3	12	12	-4.522476	11	0.0008690	***
586.3143_2.221	rel_abund_log2	b1	b3	12	12	2.296927	11	0.0423000	*
591.3894_5.174	rel_abund_log2	b1	b3	12	12	-2.279564	11	0.0436000	*
591.462_4.314	rel_abund_log2	b1	b3	12	12	4.996007	11	0.0004050	***
591.462_4.651	rel_abund_log2	b1	b3	12	12	4.759642	11	0.0005900	***
593.4778_4.664	rel_abund_log2	b1	b3	12	12	6.664055	11	0.0000354	****
594.3768_3.858	rel_abund_log2	b1	b3	12	12	2.493352	11	0.0299000	*
595.4933_4.953	rel_abund_log2	b1	b3	12	12	8.808419	11	0.0000026	****
605.1555_0.616	rel_abund_log2	b1	b3	12	12	2.914407	11	0.0141000	*
618.308_3.232	rel_abund_log2	b1	b3	12	12	2.368440	11	0.0373000	*
624.3379_0.689	rel_abund_log2	b1	b3	12	12	-3.822007	11	0.0028300	**
624.3382_0.91	rel_abund_log2	b1	b3	12	12	-2.731510	11	0.0195000	*
630.5417_10.49	rel_abund_log2	b1	b3	12	12	2.427459	11	0.0336000	*
634.3329_3.232	rel_abund_log2	b1	b3	12	12	4.244903	11	0.0013800	**
652.5879_10.611	rel_abund_log2	b1	b3	12	12	2.216051	11	0.0487000	*
653.491_10.071	rel_abund_log2	b1	b3	12	12	-2.500840	11	0.0295000	*
658.0669_0.628	rel_abund_log2	b1	b3	12	12	2.586947	11	0.0253000	*
676.6243_10.731	rel_abund_log2	b1	b3	12	12	-4.997688	11	0.0004040	***
680.5744_10.545	rel_abund_log2	b1	b3	12	12	-2.790812	11	0.0176000	*
682.5753_10.545	rel_abund_log2	b1	b3	12	12	-3.394082	11	0.0059900	**
684.6065_10.853	rel_abund_log2	b1	b3	12	12	2.741460	11	0.0192000	*
690.603_10.546	rel_abund_log2	b1	b3	12	12	-2.866297	11	0.0153000	*
696.3033_3.233	rel_abund_log2	b1	b3	12	12	3.120607	11	0.0097400	**
696.6064_10.749	rel_abund_log2	b1	b3	12	12	-4.216071	11	0.0014500	**
720.4962_7.178	rel_abund_log2	b1	b3	12	12	2.222434	11	0.0482000	*
736.5275_8.432	rel_abund_log2	b1	b3	12	12	-3.108040	11	0.0099600	**
746.5966_10.853	rel_abund_log2	b1	b3	12	12	2.532297	11	0.0279000	*
750.5436_9.042	rel_abund_log2	b1	b3	12	12	-2.428826	11	0.0335000	*
759.5646_6.287	rel_abund_log2	b1	b3	12	12	-3.359414	11	0.0063700	**
763.5516_6.749	rel_abund_log2	b1	b3	12	12	-2.605778	11	0.0244000	*
764.5798_8.343	rel_abund_log2	b1	b3	12	12	-2.697964	11	0.0207000	*
766.5385_8.385	rel_abund_log2	b1	b3	12	12	-2.278484	11	0.0437000	*
773.5806_6.749	rel_abund_log2	b1	b3	12	12	-4.202953	11	0.0014800	**
776.5438_6.762	rel_abund_log2	b1	b3	12	12	2.234046	11	0.0472000	*
780.4705_7.867	rel_abund_log2	b1	b3	12	12	-2.371311	11	0.0371000	*
786.5646_7.575	rel_abund_log2	b1	b3	12	12	-2.674363	11	0.0216000	*
789.6114_7.911	rel_abund_log2	b1	b3	12	12	-2.894935	11	0.0146000	*
790.5382_8.16	rel_abund_log2	b1	b3	12	12	-2.710232	11	0.0203000	*
790.5593_7.252	rel_abund_log2	b1	b3	12	12	2.392407	11	0.0357000	*
793.5985_8.765	rel_abund_log2	b1	b3	12	12	4.083592	11	0.0018100	**
794.5298_7.047	rel_abund_log2	b1	b3	12	12	2.470229	11	0.0311000	*
794.5464_7.772	rel_abund_log2	b1	b3	12	12	4.425982	11	0.0010200	**
795.5979_8.768	rel_abund_log2	b1	b3	12	12	2.910540	11	0.0142000	*
796.5458_7.771	rel_abund_log2	b1	b3	12	12	3.334957	11	0.0066500	**
800.5357_7.469	rel_abund_log2	b1	b3	12	12	-2.935130	11	0.0136000	*
803.6275_8.771	rel_abund_log2	b1	b3	12	12	2.380422	11	0.0365000	*
804.5762_7.774	rel_abund_log2	b1	b3	12	12	4.761839	11	0.0005880	***
804.6308_8.768	rel_abund_log2	b1	b3	12	12	2.708314	11	0.0204000	*
806.5463_7.553	rel_abund_log2	b1	b3	12	12	-2.657083	11	0.0223000	*
810.5642_6.968	rel_abund_log2	b1	b3	12	12	2.864907	11	0.0154000	*
810.5647_7.467	rel_abund_log2	b1	b3	12	12	-6.036476	11	0.0000847	****
812.5436_6.468	rel_abund_log2	b1	b3	12	12	-2.288893	11	0.0429000	*
812.58_7.898	rel_abund_log2	b1	b3	12	12	-2.972847	11	0.0127000	*
815.627_8.417	rel_abund_log2	b1	b3	12	12	-2.539421	11	0.0275000	*
818.6271_10.414	rel_abund_log2	b1	b3	12	12	2.627604	11	0.0235000	*
819.6141_8.816	rel_abund_log2	b1	b3	12	12	-2.470010	11	0.0311000	*
819.6141_9.024	rel_abund_log2	b1	b3	12	12	2.336529	11	0.0394000	*
820.4801_7.866	rel_abund_log2	b1	b3	12	12	-2.958164	11	0.0130000	*
821.6139_9.026	rel_abund_log2	b1	b3	12	12	3.130582	11	0.0095700	**
822.5774_8.941	rel_abund_log2	b1	b3	12	12	3.223471	11	0.0081100	**
824.5771_8.938	rel_abund_log2	b1	b3	12	12	2.959486	11	0.0130000	*
824.5789_7.841	rel_abund_log2	b1	b3	12	12	-5.511575	11	0.0001830	***
827.6272_7.905	rel_abund_log2	b1	b3	12	12	-6.458125	11	0.0000469	****
828.6559_10.414	rel_abund_log2	b1	b3	12	12	2.674948	11	0.0216000	*
829.6431_8.816	rel_abund_log2	b1	b3	12	12	-2.604246	11	0.0245000	*
829.6432_9.024	rel_abund_log2	b1	b3	12	12	2.546679	11	0.0272000	*
830.5815_7.188	rel_abund_log2	b1	b3	12	12	2.286961	11	0.0430000	*
831.6576_9.28	rel_abund_log2	b1	b3	12	12	-3.683310	11	0.0036000	**
831.659_9.98	rel_abund_log2	b1	b3	12	12	2.594857	11	0.0249000	*
832.6067_8.94	rel_abund_log2	b1	b3	12	12	2.286437	11	0.0430000	*
833.6294_9.395	rel_abund_log2	b1	b3	12	12	-3.469274	11	0.0052500	**
834.5643_7.242	rel_abund_log2	b1	b3	12	12	-3.396780	11	0.0059600	**
835.5322_8.336	rel_abund_log2	b1	b3	12	12	3.912013	11	0.0024300	**
836.5799_7.605	rel_abund_log2	b1	b3	12	12	-3.290278	11	0.0072000	**
838.5955_8.279	rel_abund_log2	b1	b3	12	12	-3.243569	11	0.0078200	**
838.5956_7.75	rel_abund_log2	b1	b3	12	12	-2.958585	11	0.0130000	*
838.5956_8.612	rel_abund_log2	b1	b3	12	12	-2.589499	11	0.0252000	*
840.5304_6.407	rel_abund_log2	b1	b3	12	12	-2.607587	11	0.0244000	*
840.5749_7.321	rel_abund_log2	b1	b3	12	12	-2.328660	11	0.0400000	*
840.5749_7.471	rel_abund_log2	b1	b3	12	12	-3.952872	11	0.0022600	**
840.6112_8.445	rel_abund_log2	b1	b3	12	12	-2.665196	11	0.0220000	*
841.5661_6.75	rel_abund_log2	b1	b3	12	12	-2.798294	11	0.0173000	*
841.6406_8.452	rel_abund_log2	b1	b3	12	12	-4.142839	11	0.0016400	**
842.5905_7.891	rel_abund_log2	b1	b3	12	12	-2.893307	11	0.0146000	*
843.5493_6.926	rel_abund_log2	b1	b3	12	12	-2.532832	11	0.0278000	*
843.6586_9.396	rel_abund_log2	b1	b3	12	12	-4.632265	11	0.0007260	***
844.562_8.02	rel_abund_log2	b1	b3	12	12	-2.899062	11	0.0145000	*
846.5617_8.018	rel_abund_log2	b1	b3	12	12	-2.362157	11	0.0377000	*
851.6615_10.59	rel_abund_log2	b1	b3	12	12	2.365918	11	0.0374000	*
853.619_10.659	rel_abund_log2	b1	b3	12	12	-2.959340	11	0.0130000	*
853.6428_8.12	rel_abund_log2	b1	b3	12	12	-5.439360	11	0.0002040	***
854.5519_7.576	rel_abund_log2	b1	b3	12	12	-2.759487	11	0.0186000	*
854.5918_8.021	rel_abund_log2	b1	b3	12	12	-2.640217	11	0.0230000	*
854.6714_10.418	rel_abund_log2	b1	b3	12	12	-2.934783	11	0.0136000	*
855.59_8.755	rel_abund_log2	b1	b3	12	12	2.782645	11	0.0178000	*
855.6591_9.033	rel_abund_log2	b1	b3	12	12	-3.370710	11	0.0062400	**
856.5377_7.769	rel_abund_log2	b1	b3	12	12	2.819373	11	0.0167000	*
857.6737_9.513	rel_abund_log2	b1	b3	12	12	-4.017278	11	0.0020300	**
857.6749_9.986	rel_abund_log2	b1	b3	12	12	-2.519018	11	0.0285000	*
859.6907_10.59	rel_abund_log2	b1	b3	12	12	2.507800	11	0.0291000	*
860.6919_10.384	rel_abund_log2	b1	b3	12	12	-2.220171	11	0.0484000	*
862.5261_7.47	rel_abund_log2	b1	b3	12	12	-2.442545	11	0.0327000	*
862.527_7.471	rel_abund_log2	b1	b3	12	12	-3.581277	11	0.0043100	**
862.5335_7.761	rel_abund_log2	b1	b3	12	12	3.484913	11	0.0051000	**
862.6535_10.583	rel_abund_log2	b1	b3	12	12	2.461363	11	0.0316000	*
863.5644_9.582	rel_abund_log2	b1	b3	12	12	4.513263	11	0.0008820	***
864.5747_7.263	rel_abund_log2	b1	b3	12	12	-4.194692	11	0.0015000	**
867.5493_6.755	rel_abund_log2	b1	b3	12	12	-3.270510	11	0.0074600	**
868.5303_7.476	rel_abund_log2	b1	b3	12	12	-5.161248	11	0.0003130	***
870.5618_7.814	rel_abund_log2	b1	b3	12	12	-2.517540	11	0.0286000	*
871.6147_8.768	rel_abund_log2	b1	b3	12	12	2.998878	11	0.0121000	*
871.6891_10.337	rel_abund_log2	b1	b3	12	12	-6.058263	11	0.0000821	****
872.5626_7.769	rel_abund_log2	b1	b3	12	12	4.662368	11	0.0006910	***
872.6815_10.583	rel_abund_log2	b1	b3	12	12	2.303731	11	0.0418000	*
878.5903_7.305	rel_abund_log2	b1	b3	12	12	-2.993849	11	0.0122000	*
878.5909_7.805	rel_abund_log2	b1	b3	12	12	-3.196243	11	0.0085100	**
880.5669_7.637	rel_abund_log2	b1	b3	12	12	-2.649512	11	0.0226000	*
880.6062_8.517	rel_abund_log2	b1	b3	12	12	-2.497099	11	0.0297000	*
881.6055_9.023	rel_abund_log2	b1	b3	12	12	2.446621	11	0.0324000	*
882.6209_8.855	rel_abund_log2	b1	b3	12	12	-2.384586	11	0.0362000	*
883.5565_8.118	rel_abund_log2	b1	b3	12	12	3.119995	11	0.0097500	**
883.6211_9.976	rel_abund_log2	b1	b3	12	12	2.980937	11	0.0125000	*
884.5687_8.94	rel_abund_log2	b1	b3	12	12	2.947014	11	0.0133000	*
887.5646_9.463	rel_abund_log2	b1	b3	12	12	2.692579	11	0.0209000	*
887.5991_9.03	rel_abund_log2	b1	b3	12	12	3.089693	11	0.0103000	*
888.5362_7.768	rel_abund_log2	b1	b3	12	12	3.724193	11	0.0033600	**
894.658_10.086	rel_abund_log2	b1	b3	12	12	-2.874958	11	0.0151000	*
905.6039_10.298	rel_abund_log2	b1	b3	12	12	2.489882	11	0.0300000	*
906.5828_8.614	rel_abund_log2	b1	b3	12	12	-2.212480	11	0.0490000	*
909.6368_9.99	rel_abund_log2	b1	b3	12	12	-2.678270	11	0.0215000	*
911.6461_9.397	rel_abund_log2	b1	b3	12	12	-3.091993	11	0.0102000	*
914.6592_10.621	rel_abund_log2	b1	b3	12	12	-2.615078	11	0.0240000	*
915.6327_10.293	rel_abund_log2	b1	b3	12	12	2.804097	11	0.0171000	*
918.5543_7.768	rel_abund_log2	b1	b3	12	12	4.563219	11	0.0008120	***
918.5546_7.77	rel_abund_log2	b1	b3	12	12	5.609386	11	0.0001580	***
919.2277_0.616	rel_abund_log2	b1	b3	12	12	2.779683	11	0.0179000	*
919.6483_10.59	rel_abund_log2	b1	b3	12	12	2.490412	11	0.0300000	*
920.6735_10.31	rel_abund_log2	b1	b3	12	12	-5.883455	11	0.0001060	***
922.5781_8.016	rel_abund_log2	b1	b3	12	12	-3.273357	11	0.0074200	**
922.6891_10.613	rel_abund_log2	b1	b3	12	12	-2.865719	11	0.0154000	*
923.646_9.035	rel_abund_log2	b1	b3	12	12	-3.004914	11	0.0120000	*
923.6524_10.353	rel_abund_log2	b1	b3	12	12	-4.830168	11	0.0005270	***
925.6617_9.988	rel_abund_log2	b1	b3	12	12	-2.595588	11	0.0249000	*
930.5504_7.815	rel_abund_log2	b1	b3	12	12	-3.151413	11	0.0092200	**
931.5514_9.582	rel_abund_log2	b1	b3	12	12	3.949634	11	0.0022700	**
938.5513_8.018	rel_abund_log2	b1	b3	12	12	-2.411316	11	0.0345000	*
939.6769_10.335	rel_abund_log2	b1	b3	12	12	-3.723151	11	0.0033600	**
940.5496_7.764	rel_abund_log2	b1	b3	12	12	2.698203	11	0.0207000	*
945.6385_9.975	rel_abund_log2	b1	b3	12	12	2.334111	11	0.0396000	*
959.6018_9.026	rel_abund_log2	b1	b3	12	12	2.230706	11	0.0475000	*
967.6322_9.976	rel_abund_log2	b1	b3	12	12	2.648209	11	0.0227000	*
968.581_8.937	rel_abund_log2	b1	b3	12	12	2.602143	11	0.0246000	*
971.6542_9.988	rel_abund_log2	b1	b3	12	12	-3.216284	11	0.0082100	**
983.6184_9.976	rel_abund_log2	b1	b3	12	12	-2.773685	11	0.0181000	*
983.6185_9.993	rel_abund_log2	b1	b3	12	12	-2.688211	11	0.0211000	*
984.5483_8.018	rel_abund_log2	b1	b3	12	12	-2.450959	11	0.0322000	*
985.616_9.034	rel_abund_log2	b1	b3	12	12	-2.314878	11	0.0409000	*
993.6487_10.29	rel_abund_log2	b1	b3	12	12	3.001124	11	0.0121000	*
993.6488_9.986	rel_abund_log2	b1	b3	12	12	-3.060578	11	0.0108000	*

# how many are significant?
nrow(red_t.test_paired_sig)

## [1] 217

Remove BG diet

# let's grab both datasets for features sig in pre v post red and pre v post control and combine them
fulljoin_t.test_redANDctrl <- full_join(red_t.test_paired_sig, ctrl_t.test_paired_sig,
                                        by = "mz_rt",
                                        suffix = c(".red", ".ctrl"))

# number of features in combined list
nrow(fulljoin_t.test_redANDctrl)

## [1] 317

Let’s try and remove features significant due to the background diet

sig_paired_red_rmBG <- fulljoin_t.test_redANDctrl %>%
  # add a column to account for direction
  mutate(sign = statistic.red * statistic.ctrl) %>%
  # replace NAs in the sign column with 0
  mutate(sign = replace_na(sign, 0)) %>%
  # replace NAs in the statistic.ctrl column to 0
  mutate(statistic.ctrl = replace_na(statistic.ctrl, 0)) %>%
  # filter for columns that are either negative (means ctrl and tomato are going in opposite dir) or where the stat.ctrl col is 0 (so we don't remove features that are just not present in the sig control list)
  filter((sign < 0 | statistic.ctrl == 0))

# number of features without bg diet effect
nrow(sig_paired_red_rmBG)

## [1] 95

How many BG-diet-related features did we remove from the list of significant beta features?

nrow(red_t.test_paired_sig) - nrow(sig_paired_red_rmBG)

## [1] 122

ANOVA overlap

Keep sig features in t-test that have a match in sig ANOVA. Let’s take our new feature list (background diet effect removed)

# select only features from paired list that have a match in ANOVA list
red_sig_ANOVA_overlap_paired <- inner_join(sig_paired_red_rmBG,
                                            trt_tukeyHSD_sig,
                                            by = "mz_rt",
                                            suffix = c(".t-test", ".tukeys"))

# features overlapping with sig ANOVA
unique(red_sig_ANOVA_overlap_paired$mz_rt)

##  [1] "1002.5196_7.769" "1561.2028_9.038" "561.2272_6.889"  "736.5275_8.432" 
##  [5] "764.5798_8.343"  "800.5357_7.469"  "824.5771_8.938"  "853.619_10.659" 
##  [9] "857.6749_9.986"  "862.5335_7.761"  "863.5644_9.582"  "868.5303_7.476" 
## [13] "887.5991_9.03"   "909.6368_9.99"   "915.6327_10.293" "940.5496_7.764" 
## [17] "945.6385_9.975"  "983.6184_9.976"  "983.6185_9.993"

Mummichog list

Pulling features that are significant in paired t-test due to treatment, not due to background diet.

paired_red_for_mummichog <- sig_paired_red_rmBG %>%
  dplyr::select(mz_rt,
         p.red,
         statistic.red) %>%
  separate(col = mz_rt,
           into = c("m/z", "rt"),
           sep = "_") %>%
  rename("p-value" = "p.red") %>%
  rename("t-score" = "statistic.red")

write_csv(paired_red_for_mummichog,
          "for mummichog analysis/t-test-res-red-paired.csv")

Paired tomato

# run paired t-tests for control intervention
tomato_t.test_paired <- df_for_stats %>%
  filter(tomato_or_control == "tomato") %>%
 dplyr::select(subject, period, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ period, 
         paired = TRUE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()

Statistically significant features

# which features are significant?
tomato_t.test_paired_sig <- tomato_t.test_paired %>%
  filter(p < 0.05)
kable(tomato_t.test_paired_sig)

mz_rt	.y.	group1	group2	n1	n2	statistic	df	p	p.signif
1002.5196_7.769	rel_abund_log2	b1	b3	24	24	4.363569	23	0.0002270	***
1008.5435_7.813	rel_abund_log2	b1	b3	24	24	-2.718844	23	0.0122000	*
1016.7241_9.858	rel_abund_log2	b1	b3	24	24	2.324759	23	0.0293000	*
103.04_0.651	rel_abund_log2	b1	b3	24	24	2.612068	23	0.0156000	*
1061.6776_3.089	rel_abund_log2	b1	b3	24	24	2.500347	23	0.0200000	*
1135.6242_2.73	rel_abund_log2	b1	b3	24	24	2.252609	23	0.0341000	*
117.0557_0.669	rel_abund_log2	b1	b3	24	24	4.301816	23	0.0002650	***
1235.798_10.088	rel_abund_log2	b1	b3	24	24	3.621783	23	0.0014300	**
1261.8136_10.084	rel_abund_log2	b1	b3	24	24	-2.623333	23	0.0152000	*
1263.8295_10.575	rel_abund_log2	b1	b3	24	24	2.387722	23	0.0256000	*
1279.8241_10.52	rel_abund_log2	b1	b3	24	24	3.745093	23	0.0010600	**
1334.2417_10.855	rel_abund_log2	b1	b3	24	24	3.785577	23	0.0009570	***
1335.245_10.854	rel_abund_log2	b1	b3	24	24	4.241285	23	0.0003090	***
1554.1226_7.773	rel_abund_log2	b1	b3	24	24	4.382053	23	0.0002170	***
1555.1258_7.773	rel_abund_log2	b1	b3	24	24	3.206343	23	0.0039200	**
1558.1192_8.016	rel_abund_log2	b1	b3	24	24	-2.354922	23	0.0274000	*
1561.2028_9.038	rel_abund_log2	b1	b3	24	24	-5.762764	23	0.0000072	****
1579.1408_6.75	rel_abund_log2	b1	b3	24	24	-3.006221	23	0.0063000	**
1584.1204_6.981	rel_abund_log2	b1	b3	24	24	-2.145271	23	0.0427000	*
1585.1238_6.981	rel_abund_log2	b1	b3	24	24	-2.143270	23	0.0429000	*
1598.0913_6.94	rel_abund_log2	b1	b3	24	24	-2.422202	23	0.0237000	*
1598.1714_7.758	rel_abund_log2	b1	b3	24	24	2.904506	23	0.0079900	**
1599.0952_6.94	rel_abund_log2	b1	b3	24	24	-2.215507	23	0.0369000	*
1600.0951_6.94	rel_abund_log2	b1	b3	24	24	-2.689580	23	0.0131000	*
1602.1228_7.339	rel_abund_log2	b1	b3	24	24	-2.403199	23	0.0247000	*
1612.1504_7.187	rel_abund_log2	b1	b3	24	24	2.617357	23	0.0154000	*
1616.1136_7.768	rel_abund_log2	b1	b3	24	24	4.301321	23	0.0002660	***
1632.139_7.776	rel_abund_log2	b1	b3	24	24	3.622438	23	0.0014300	**
1633.1422_7.775	rel_abund_log2	b1	b3	24	24	4.522504	23	0.0001530	***
1654.1535_8.02	rel_abund_log2	b1	b3	24	24	-2.929780	23	0.0075300	**
1655.1569_8.02	rel_abund_log2	b1	b3	24	24	-3.281654	23	0.0032700	**
1664.3521_10.59	rel_abund_log2	b1	b3	24	24	4.040890	23	0.0005080	***
1665.3553_10.589	rel_abund_log2	b1	b3	24	24	3.286385	23	0.0032300	**
1666.3553_10.589	rel_abund_log2	b1	b3	24	24	3.182997	23	0.0041400	**
1669.2155_8.46	rel_abund_log2	b1	b3	24	24	-2.172830	23	0.0403000	*
1670.349_9.987	rel_abund_log2	b1	b3	24	24	-3.026759	23	0.0060000	**
1674.3803_10.59	rel_abund_log2	b1	b3	24	24	3.712049	23	0.0011500	**
1675.384_10.589	rel_abund_log2	b1	b3	24	24	3.620181	23	0.0014400	**
1685.1732_8.11	rel_abund_log2	b1	b3	24	24	2.101121	23	0.0468000	*
172.9913_0.642	rel_abund_log2	b1	b3	24	24	3.198641	23	0.0039900	**
188.9862_0.644	rel_abund_log2	b1	b3	24	24	-3.065903	23	0.0054700	**
201.0226_0.688	rel_abund_log2	b1	b3	24	24	3.998360	23	0.0005650	***
201.0575_0.644	rel_abund_log2	b1	b3	24	24	2.171078	23	0.0405000	*
213.0223_0.689	rel_abund_log2	b1	b3	24	24	-2.881431	23	0.0084300	**
226.9658_0.499	rel_abund_log2	b1	b3	24	24	-2.307105	23	0.0304000	*
229.0537_0.703	rel_abund_log2	b1	b3	24	24	-2.223511	23	0.0363000	*
230.9967_0.644	rel_abund_log2	b1	b3	24	24	-2.398109	23	0.0250000	*
239.0923_0.975	rel_abund_log2	b1	b3	24	24	2.249157	23	0.0344000	*
244.908_0.706	rel_abund_log2	b1	b3	24	24	-3.051810	23	0.0056600	**
311.2224_2.465	rel_abund_log2	b1	b3	24	24	3.457428	23	0.0021400	**
316.9477_0.518	rel_abund_log2	b1	b3	24	24	-2.481219	23	0.0208000	*
343.9948_0.639	rel_abund_log2	b1	b3	24	24	-2.534062	23	0.0185000	*
352.0856_0.687	rel_abund_log2	b1	b3	24	24	-5.668731	23	0.0000090	****
397.3681_5	rel_abund_log2	b1	b3	24	24	4.364590	23	0.0002270	***
411.3837_5.349	rel_abund_log2	b1	b3	24	24	8.048607	23	0.0000000	****
413.1998_0.688	rel_abund_log2	b1	b3	24	24	2.312217	23	0.0301000	*
436.2827_3.522	rel_abund_log2	b1	b3	24	24	-3.158151	23	0.0044000	**
437.0542_0.688	rel_abund_log2	b1	b3	24	24	-4.140244	23	0.0003970	***
441.3942_4.718	rel_abund_log2	b1	b3	24	24	5.755661	23	0.0000073	****
447.3471_4.631	rel_abund_log2	b1	b3	24	24	2.640290	23	0.0146000	*
449.3627_4.663	rel_abund_log2	b1	b3	24	24	2.657712	23	0.0141000	*
462.1763_0.655	rel_abund_log2	b1	b3	24	24	-6.698636	23	0.0000008	****
467.3735_4.306	rel_abund_log2	b1	b3	24	24	2.465112	23	0.0216000	*
476.2777_2.847	rel_abund_log2	b1	b3	24	24	2.596805	23	0.0161000	*
478.2933_3.354	rel_abund_log2	b1	b3	24	24	5.056524	23	0.0000405	****
504.3089_2.726	rel_abund_log2	b1	b3	24	24	3.204509	23	0.0039400	**
506.3245_3.232	rel_abund_log2	b1	b3	24	24	3.574233	23	0.0016100	**
507.223_1.7	rel_abund_log2	b1	b3	24	24	-11.228326	23	0.0000000	****
507.223_2.273	rel_abund_log2	b1	b3	24	24	-9.093738	23	0.0000000	****
511.3997_4.453	rel_abund_log2	b1	b3	24	24	2.746641	23	0.0115000	*
517.2436_2.68	rel_abund_log2	b1	b3	24	24	2.524262	23	0.0190000	*
519.347_6.861	rel_abund_log2	b1	b3	24	24	2.216985	23	0.0368000	*
524.3351_3.376	rel_abund_log2	b1	b3	24	24	-2.734986	23	0.0118000	*
526.3144_2.683	rel_abund_log2	b1	b3	24	24	-2.997438	23	0.0064300	**
528.2631_0.691	rel_abund_log2	b1	b3	24	24	2.972412	23	0.0068200	**
537.4153_4.24	rel_abund_log2	b1	b3	24	24	2.795157	23	0.0103000	*
539.4309_4.416	rel_abund_log2	b1	b3	24	24	3.598005	23	0.0015200	**
551.3582_4.841	rel_abund_log2	b1	b3	24	24	-9.941869	23	0.0000000	****
554.3456_3.441	rel_abund_log2	b1	b3	24	24	-3.608189	23	0.0014800	**
556.299_2.728	rel_abund_log2	b1	b3	24	24	2.192282	23	0.0387000	*
557.4566_6.309	rel_abund_log2	b1	b3	24	24	4.515541	23	0.0001560	***
557.457_6.865	rel_abund_log2	b1	b3	24	24	3.545420	23	0.0017300	**
558.3148_3.234	rel_abund_log2	b1	b3	24	24	3.885466	23	0.0007470	***
559.4719_6.867	rel_abund_log2	b1	b3	24	24	6.625960	23	0.0000009	****
560.2269_6.891	rel_abund_log2	b1	b3	24	24	-2.847245	23	0.0091200	**
564.3308_2.727	rel_abund_log2	b1	b3	24	24	2.162660	23	0.0412000	*
566.3462_3.232	rel_abund_log2	b1	b3	24	24	4.767304	23	0.0000831	****
567.4621_4.643	rel_abund_log2	b1	b3	24	24	6.042322	23	0.0000037	****
573.4515_4.266	rel_abund_log2	b1	b3	24	24	6.048715	23	0.0000036	****
573.4515_4.873	rel_abund_log2	b1	b3	24	24	4.490611	23	0.0001660	***
575.4672_5.354	rel_abund_log2	b1	b3	24	24	7.374904	23	0.0000002	****
575.467_4.662	rel_abund_log2	b1	b3	24	24	6.917587	23	0.0000005	****
577.3738_4.918	rel_abund_log2	b1	b3	24	24	-9.700780	23	0.0000000	****
579.3893_5.557	rel_abund_log2	b1	b3	24	24	-8.857138	23	0.0000000	****
583.1735_0.618	rel_abund_log2	b1	b3	24	24	2.308349	23	0.0303000	*
591.3894_5.174	rel_abund_log2	b1	b3	24	24	-5.036246	23	0.0000426	****
591.462_4.314	rel_abund_log2	b1	b3	24	24	4.970156	23	0.0000502	****
591.462_4.651	rel_abund_log2	b1	b3	24	24	6.404172	23	0.0000016	****
593.4778_4.664	rel_abund_log2	b1	b3	24	24	8.493080	23	0.0000000	****
594.3768_3.858	rel_abund_log2	b1	b3	24	24	3.620389	23	0.0014400	**
595.4933_4.953	rel_abund_log2	b1	b3	24	24	14.557359	23	0.0000000	****
600.5119_9.48	rel_abund_log2	b1	b3	24	24	-2.683104	23	0.0133000	*
605.1555_0.616	rel_abund_log2	b1	b3	24	24	4.092346	23	0.0004470	***
618.308_3.232	rel_abund_log2	b1	b3	24	24	3.341788	23	0.0028300	**
619.2886_2.757	rel_abund_log2	b1	b3	24	24	2.243276	23	0.0348000	*
624.3379_0.689	rel_abund_log2	b1	b3	24	24	-4.761801	23	0.0000843	****
624.3382_0.91	rel_abund_log2	b1	b3	24	24	-2.332559	23	0.0288000	*
630.5417_10.49	rel_abund_log2	b1	b3	24	24	2.228670	23	0.0359000	*
632.3174_2.729	rel_abund_log2	b1	b3	24	24	2.142473	23	0.0430000	*
634.3329_3.232	rel_abund_log2	b1	b3	24	24	4.891200	23	0.0000611	****
640.5796_10.729	rel_abund_log2	b1	b3	24	24	-3.143170	23	0.0045600	**
643.1067_0.611	rel_abund_log2	b1	b3	24	24	2.161843	23	0.0413000	*
653.491_10.071	rel_abund_log2	b1	b3	24	24	-2.405533	23	0.0246000	*
654.5589_10.54	rel_abund_log2	b1	b3	24	24	2.211313	23	0.0372000	*
656.5586_10.54	rel_abund_log2	b1	b3	24	24	2.415822	23	0.0240000	*
656.575_10.694	rel_abund_log2	b1	b3	24	24	3.219790	23	0.0037900	**
658.0669_0.628	rel_abund_log2	b1	b3	24	24	2.725567	23	0.0121000	*
664.5877_10.541	rel_abund_log2	b1	b3	24	24	3.142568	23	0.0045600	**
674.6039_10.834	rel_abund_log2	b1	b3	24	24	-4.290434	23	0.0002730	***
676.6243_10.731	rel_abund_log2	b1	b3	24	24	-6.593231	23	0.0000010	****
678.6402_10.885	rel_abund_log2	b1	b3	24	24	-3.188411	23	0.0040900	**
680.0486_0.63	rel_abund_log2	b1	b3	24	24	-2.070573	23	0.0498000	*
680.5744_10.545	rel_abund_log2	b1	b3	24	24	-3.160390	23	0.0043700	**
682.5753_10.545	rel_abund_log2	b1	b3	24	24	-2.901487	23	0.0080400	**
682.6332_10.835	rel_abund_log2	b1	b3	24	24	-3.875103	23	0.0007670	***
684.6065_10.853	rel_abund_log2	b1	b3	24	24	4.358717	23	0.0002300	***
690.603_10.546	rel_abund_log2	b1	b3	24	24	-3.129321	23	0.0047100	**
696.3033_3.233	rel_abund_log2	b1	b3	24	24	4.038067	23	0.0005120	***
696.6064_10.749	rel_abund_log2	b1	b3	24	24	-5.829105	23	0.0000061	****
698.3189_3.775	rel_abund_log2	b1	b3	24	24	-2.244151	23	0.0347000	*
698.5121_7.962	rel_abund_log2	b1	b3	24	24	-2.546788	23	0.0180000	*
705.5178_5.308	rel_abund_log2	b1	b3	24	24	-3.584788	23	0.0015700	**
711.6251_10.854	rel_abund_log2	b1	b3	24	24	2.727884	23	0.0120000	*
716.5225_8.139	rel_abund_log2	b1	b3	24	24	3.376880	23	0.0026000	**
717.5179_5.106	rel_abund_log2	b1	b3	24	24	-2.633705	23	0.0148000	*
721.549_6	rel_abund_log2	b1	b3	24	24	-4.236895	23	0.0003120	***
722.5123_7.861	rel_abund_log2	b1	b3	24	24	-3.736148	23	0.0010800	**
724.5276_8.3	rel_abund_log2	b1	b3	24	24	-3.470349	23	0.0020700	**
735.5203_5.846	rel_abund_log2	b1	b3	24	24	2.355306	23	0.0274000	*
736.5275_8.432	rel_abund_log2	b1	b3	24	24	-3.387484	23	0.0025300	**
740.5225_7.515	rel_abund_log2	b1	b3	24	24	2.785093	23	0.0105000	*
744.5538_9.321	rel_abund_log2	b1	b3	24	24	3.624494	23	0.0014200	**
746.5966_10.853	rel_abund_log2	b1	b3	24	24	4.830749	23	0.0000710	****
748.5277_8.014	rel_abund_log2	b1	b3	24	24	-2.281994	23	0.0321000	*
749.5806_6.906	rel_abund_log2	b1	b3	24	24	-2.069449	23	0.0499000	*
750.5432_8.694	rel_abund_log2	b1	b3	24	24	-2.108353	23	0.0461000	*
750.5436_9.042	rel_abund_log2	b1	b3	24	24	-4.050904	23	0.0004960	***
752.5591_9.514	rel_abund_log2	b1	b3	24	24	-2.279307	23	0.0322000	*
759.5646_6.287	rel_abund_log2	b1	b3	24	24	-4.619967	23	0.0001200	***
761.5803_7.053	rel_abund_log2	b1	b3	24	24	-2.970127	23	0.0068500	**
762.5643_8.206	rel_abund_log2	b1	b3	24	24	-2.889935	23	0.0082600	**
762.6216_10.854	rel_abund_log2	b1	b3	24	24	3.036895	23	0.0058600	**
763.5516_6.749	rel_abund_log2	b1	b3	24	24	-2.775025	23	0.0108000	*
764.5225_7.43	rel_abund_log2	b1	b3	24	24	2.222037	23	0.0364000	*
764.5798_8.343	rel_abund_log2	b1	b3	24	24	-3.670107	23	0.0012700	**
765.5735_6.098	rel_abund_log2	b1	b3	24	24	-2.360399	23	0.0271000	*
771.5646_6.096	rel_abund_log2	b1	b3	24	24	-3.234385	23	0.0036600	**
773.5806_6.749	rel_abund_log2	b1	b3	24	24	-3.568133	23	0.0016300	**
775.5965_7.563	rel_abund_log2	b1	b3	24	24	-2.125432	23	0.0445000	*
776.5351_7.576	rel_abund_log2	b1	b3	24	24	-3.164678	23	0.0043300	**
776.5354_7.574	rel_abund_log2	b1	b3	24	24	-3.315384	23	0.0030200	**
777.6118_7.986	rel_abund_log2	b1	b3	24	24	-3.243935	23	0.0035800	**
780.4705_7.867	rel_abund_log2	b1	b3	24	24	-4.315718	23	0.0002560	***
784.549_6.62	rel_abund_log2	b1	b3	24	24	2.270647	23	0.0328000	*
786.5646_7.575	rel_abund_log2	b1	b3	24	24	-4.533708	23	0.0001490	***
788.5795_7.719	rel_abund_log2	b1	b3	24	24	-2.124118	23	0.0446000	*
789.6114_7.911	rel_abund_log2	b1	b3	24	24	-4.741910	23	0.0000886	****
789.6116_8.177	rel_abund_log2	b1	b3	24	24	-3.725136	23	0.0011100	**
790.4993_7.863	rel_abund_log2	b1	b3	24	24	-3.861659	23	0.0007930	***
790.5953_8.511	rel_abund_log2	b1	b3	24	24	3.705806	23	0.0011600	**
792.5765_7.938	rel_abund_log2	b1	b3	24	24	-2.111977	23	0.0458000	*
792.8596_0.515	rel_abund_log2	b1	b3	24	24	-2.089691	23	0.0479000	*
793.5985_8.748	rel_abund_log2	b1	b3	24	24	2.378696	23	0.0261000	*
793.5985_8.765	rel_abund_log2	b1	b3	24	24	4.680943	23	0.0001030	***
794.5464_7.772	rel_abund_log2	b1	b3	24	24	4.067306	23	0.0004760	***
795.5979_8.768	rel_abund_log2	b1	b3	24	24	2.572121	23	0.0170000	*
796.5458_7.771	rel_abund_log2	b1	b3	24	24	2.976022	23	0.0067600	**
798.5279_6.032	rel_abund_log2	b1	b3	24	24	-2.165916	23	0.0409000	*
800.5357_7.469	rel_abund_log2	b1	b3	24	24	-4.605652	23	0.0001240	***
803.6275_8.749	rel_abund_log2	b1	b3	24	24	2.598700	23	0.0161000	*
803.6275_8.771	rel_abund_log2	b1	b3	24	24	3.584389	23	0.0015700	**
804.5762_7.774	rel_abund_log2	b1	b3	24	24	5.059518	23	0.0000402	****
804.6308_8.768	rel_abund_log2	b1	b3	24	24	4.044895	23	0.0005030	***
806.5463_7.553	rel_abund_log2	b1	b3	24	24	-2.935885	23	0.0074200	**
808.5018_9.051	rel_abund_log2	b1	b3	24	24	-3.201062	23	0.0039700	**
810.5642_6.968	rel_abund_log2	b1	b3	24	24	2.438649	23	0.0229000	*
810.5647_7.467	rel_abund_log2	b1	b3	24	24	-7.482269	23	0.0000001	****
812.5436_6.468	rel_abund_log2	b1	b3	24	24	-4.271119	23	0.0002860	***
812.58_7.898	rel_abund_log2	b1	b3	24	24	-4.973914	23	0.0000498	****
814.5957_8.732	rel_abund_log2	b1	b3	24	24	-2.604246	23	0.0159000	*
815.627_8.417	rel_abund_log2	b1	b3	24	24	-3.483325	23	0.0020100	**
816.5751_7.553	rel_abund_log2	b1	b3	24	24	-2.742876	23	0.0116000	*
818.5302_6.479	rel_abund_log2	b1	b3	24	24	-2.365591	23	0.0268000	*
818.5304_6.915	rel_abund_log2	b1	b3	24	24	-2.557207	23	0.0176000	*
818.5462_7.192	rel_abund_log2	b1	b3	24	24	2.835585	23	0.0093700	**
818.5462_7.337	rel_abund_log2	b1	b3	24	24	-2.154764	23	0.0419000	*
818.6271_10.414	rel_abund_log2	b1	b3	24	24	5.341349	23	0.0000201	****
819.6141_8.816	rel_abund_log2	b1	b3	24	24	-5.279463	23	0.0000234	****
819.6141_9.024	rel_abund_log2	b1	b3	24	24	4.393267	23	0.0002110	***
820.4801_7.866	rel_abund_log2	b1	b3	24	24	-3.549551	23	0.0017100	**
820.627_10.414	rel_abund_log2	b1	b3	24	24	3.803031	23	0.0009160	***
821.6139_9.026	rel_abund_log2	b1	b3	24	24	5.291687	23	0.0000227	****
822.5279_5.875	rel_abund_log2	b1	b3	24	24	-2.287061	23	0.0317000	*
822.5774_8.941	rel_abund_log2	b1	b3	24	24	4.138827	23	0.0003980	***
823.6294_9.979	rel_abund_log2	b1	b3	24	24	4.012376	23	0.0005460	***
824.5433_5.925	rel_abund_log2	b1	b3	24	24	-3.026049	23	0.0060100	**
824.5771_8.938	rel_abund_log2	b1	b3	24	24	4.416886	23	0.0001990	***
824.5789_7.841	rel_abund_log2	b1	b3	24	24	-7.420025	23	0.0000002	****
826.5606_6.953	rel_abund_log2	b1	b3	24	24	-3.202727	23	0.0039500	**
827.6272_7.905	rel_abund_log2	b1	b3	24	24	-6.361103	23	0.0000017	****
828.5668_8.612	rel_abund_log2	b1	b3	24	24	-3.243486	23	0.0035900	**
828.5759_7.19	rel_abund_log2	b1	b3	24	24	3.793244	23	0.0009390	***
828.5759_7.339	rel_abund_log2	b1	b3	24	24	-2.316699	23	0.0298000	*
828.6559_10.414	rel_abund_log2	b1	b3	24	24	4.139645	23	0.0003980	***
829.6431_8.816	rel_abund_log2	b1	b3	24	24	-3.916073	23	0.0006930	***
829.6432_9.024	rel_abund_log2	b1	b3	24	24	4.889329	23	0.0000614	****
830.5815_7.188	rel_abund_log2	b1	b3	24	24	3.701337	23	0.0011800	**
831.6576_9.28	rel_abund_log2	b1	b3	24	24	-5.586808	23	0.0000110	****
831.659_9.98	rel_abund_log2	b1	b3	24	24	4.610630	23	0.0001230	***
832.6067_8.94	rel_abund_log2	b1	b3	24	24	3.470206	23	0.0020700	**
832.6428_10.551	rel_abund_log2	b1	b3	24	24	-2.586830	23	0.0165000	*
833.5544_7.557	rel_abund_log2	b1	b3	24	24	-2.973553	23	0.0068000	**
833.6294_9.395	rel_abund_log2	b1	b3	24	24	-5.143266	23	0.0000327	****
834.5643_7.242	rel_abund_log2	b1	b3	24	24	-4.121127	23	0.0004160	***
835.5322_8.336	rel_abund_log2	b1	b3	24	24	5.594273	23	0.0000108	****
836.5434_6.295	rel_abund_log2	b1	b3	24	24	-2.920817	23	0.0076900	**
836.5799_7.605	rel_abund_log2	b1	b3	24	24	-2.967345	23	0.0069000	**
837.6571_10.552	rel_abund_log2	b1	b3	24	24	-3.419456	23	0.0023500	**
838.5955_8.279	rel_abund_log2	b1	b3	24	24	-3.303126	23	0.0031100	**
838.5956_8.612	rel_abund_log2	b1	b3	24	24	-3.747623	23	0.0010500	**
840.5304_6.407	rel_abund_log2	b1	b3	24	24	-4.255831	23	0.0002980	***
840.5749_7.321	rel_abund_log2	b1	b3	24	24	-4.007795	23	0.0005520	***
840.5749_7.471	rel_abund_log2	b1	b3	24	24	-6.173804	23	0.0000027	****
840.6112_8.445	rel_abund_log2	b1	b3	24	24	-2.854226	23	0.0089800	**
841.5661_6.75	rel_abund_log2	b1	b3	24	24	-3.947521	23	0.0006410	***
841.6406_8.452	rel_abund_log2	b1	b3	24	24	-5.937106	23	0.0000047	****
842.5306_8.773	rel_abund_log2	b1	b3	24	24	-2.798642	23	0.0102000	*
842.5905_7.891	rel_abund_log2	b1	b3	24	24	-5.270597	23	0.0000239	****
843.5493_6.926	rel_abund_log2	b1	b3	24	24	-2.481248	23	0.0208000	*
843.5839_7.561	rel_abund_log2	b1	b3	24	24	-2.410784	23	0.0243000	*
843.6586_9.396	rel_abund_log2	b1	b3	24	24	-6.449425	23	0.0000014	****
844.562_8.02	rel_abund_log2	b1	b3	24	24	-3.449329	23	0.0021800	**
846.5617_8.018	rel_abund_log2	b1	b3	24	24	-2.789544	23	0.0104000	*
847.6304_9.032	rel_abund_log2	b1	b3	24	24	-3.122384	23	0.0047900	**
847.6455_10.298	rel_abund_log2	b1	b3	24	24	4.391609	23	0.0002120	***
848.5114_9.046	rel_abund_log2	b1	b3	24	24	-3.994982	23	0.0005700	***
848.577_8.458	rel_abund_log2	b1	b3	24	24	-2.656046	23	0.0141000	*
849.6458_10.294	rel_abund_log2	b1	b3	24	24	2.329912	23	0.0289000	*
849.6462_9.988	rel_abund_log2	b1	b3	24	24	-3.875267	23	0.0007660	***
849.6616_10.589	rel_abund_log2	b1	b3	24	24	4.030695	23	0.0005210	***
850.5595_6.407	rel_abund_log2	b1	b3	24	24	-3.911912	23	0.0007000	***
851.6615_10.59	rel_abund_log2	b1	b3	24	24	3.776627	23	0.0009780	***
853.619_10.659	rel_abund_log2	b1	b3	24	24	-5.096100	23	0.0000368	****
853.6428_8.12	rel_abund_log2	b1	b3	24	24	-7.617053	23	0.0000001	****
854.5519_7.576	rel_abund_log2	b1	b3	24	24	-4.846028	23	0.0000684	****
854.5906_7.701	rel_abund_log2	b1	b3	24	24	-4.179896	23	0.0003600	***
854.5918_8.021	rel_abund_log2	b1	b3	24	24	-3.994043	23	0.0005710	***
854.6714_10.418	rel_abund_log2	b1	b3	24	24	-3.500019	23	0.0019300	**
855.59_8.755	rel_abund_log2	b1	b3	24	24	3.909666	23	0.0007040	***
855.6591_9.033	rel_abund_log2	b1	b3	24	24	-4.526102	23	0.0001520	***
856.5377_7.769	rel_abund_log2	b1	b3	24	24	2.512403	23	0.0195000	*
856.6051_8.21	rel_abund_log2	b1	b3	24	24	2.359549	23	0.0272000	*
856.6871_10.641	rel_abund_log2	b1	b3	24	24	4.058732	23	0.0004860	***
857.5409_7.768	rel_abund_log2	b1	b3	24	24	2.214898	23	0.0369000	*
857.6737_9.513	rel_abund_log2	b1	b3	24	24	-7.688097	23	0.0000001	****
857.6746_10.294	rel_abund_log2	b1	b3	24	24	4.053619	23	0.0004920	***
857.6749_9.986	rel_abund_log2	b1	b3	24	24	-4.064599	23	0.0004790	***
859.6907_10.59	rel_abund_log2	b1	b3	24	24	4.341753	23	0.0002400	***
860.6919_10.384	rel_abund_log2	b1	b3	24	24	-4.827954	23	0.0000715	****
861.5449_6.542	rel_abund_log2	b1	b3	24	24	2.361042	23	0.0271000	*
862.5261_7.47	rel_abund_log2	b1	b3	24	24	-4.463672	23	0.0001770	***
862.527_7.471	rel_abund_log2	b1	b3	24	24	-5.396469	23	0.0000175	****
862.5335_7.761	rel_abund_log2	b1	b3	24	24	3.285278	23	0.0032400	**
862.6535_10.583	rel_abund_log2	b1	b3	24	24	3.344010	23	0.0028200	**
862.664_10.445	rel_abund_log2	b1	b3	24	24	-2.098728	23	0.0470000	*
863.5644_9.582	rel_abund_log2	b1	b3	24	24	8.571089	23	0.0000000	****
863.6765_10.683	rel_abund_log2	b1	b3	24	24	-2.120187	23	0.0450000	*
864.5747_7.263	rel_abund_log2	b1	b3	24	24	-4.530838	23	0.0001500	***
865.6758_10.678	rel_abund_log2	b1	b3	24	24	-2.160360	23	0.0414000	*
866.6268_8.879	rel_abund_log2	b1	b3	24	24	2.523475	23	0.0190000	*
867.5493_6.755	rel_abund_log2	b1	b3	24	24	-3.594047	23	0.0015300	**
868.5303_7.476	rel_abund_log2	b1	b3	24	24	-5.165113	23	0.0000310	****
870.5618_7.814	rel_abund_log2	b1	b3	24	24	-3.772769	23	0.0009870	***
871.5805_8.017	rel_abund_log2	b1	b3	24	24	-3.065450	23	0.0054800	**
871.6147_8.768	rel_abund_log2	b1	b3	24	24	3.827734	23	0.0008620	***
871.6891_10.337	rel_abund_log2	b1	b3	24	24	-8.679015	23	0.0000000	****
872.5626_7.769	rel_abund_log2	b1	b3	24	24	5.230233	23	0.0000264	****
872.5955_8.863	rel_abund_log2	b1	b3	24	24	-2.088552	23	0.0480000	*
872.6815_10.583	rel_abund_log2	b1	b3	24	24	3.650773	23	0.0013300	**
873.5954_8.46	rel_abund_log2	b1	b3	24	24	-2.096182	23	0.0473000	*
874.559_6.259	rel_abund_log2	b1	b3	24	24	-2.919313	23	0.0077200	**
874.6643_9.99	rel_abund_log2	b1	b3	24	24	-2.695984	23	0.0129000	*
876.5748_6.674	rel_abund_log2	b1	b3	24	24	-2.894907	23	0.0081700	**
878.5903_7.305	rel_abund_log2	b1	b3	24	24	-5.264773	23	0.0000242	****
878.5909_7.805	rel_abund_log2	b1	b3	24	24	-3.463762	23	0.0021100	**
879.5263_6.489	rel_abund_log2	b1	b3	24	24	-2.945916	23	0.0072500	**
880.5375_7.198	rel_abund_log2	b1	b3	24	24	2.342301	23	0.0282000	*
880.6062_8.116	rel_abund_log2	b1	b3	24	24	-2.255782	23	0.0339000	*
880.6062_8.517	rel_abund_log2	b1	b3	24	24	-3.721959	23	0.0011200	**
881.5397_7.202	rel_abund_log2	b1	b3	24	24	2.787655	23	0.0105000	*
881.6055_9.023	rel_abund_log2	b1	b3	24	24	4.164691	23	0.0003730	***
882.6209_8.855	rel_abund_log2	b1	b3	24	24	-3.568648	23	0.0016300	**
883.5331_7.753	rel_abund_log2	b1	b3	24	24	-3.287512	23	0.0032300	**
883.5565_8.118	rel_abund_log2	b1	b3	24	24	3.076754	23	0.0053300	**
883.6211_9.976	rel_abund_log2	b1	b3	24	24	4.957314	23	0.0000519	****
884.5687_8.94	rel_abund_log2	b1	b3	24	24	4.617911	23	0.0001210	***
885.7055_10.58	rel_abund_log2	b1	b3	24	24	-2.949622	23	0.0071900	**
887.5646_9.463	rel_abund_log2	b1	b3	24	24	2.747165	23	0.0115000	*
887.5991_9.03	rel_abund_log2	b1	b3	24	24	6.062154	23	0.0000035	****
888.5362_7.768	rel_abund_log2	b1	b3	24	24	3.162758	23	0.0043500	**
889.617_9.974	rel_abund_log2	b1	b3	24	24	3.770904	23	0.0009920	***
890.5649_8.939	rel_abund_log2	b1	b3	24	24	3.445767	23	0.0022000	**
896.5624_7.191	rel_abund_log2	b1	b3	24	24	3.417623	23	0.0023600	**
896.6737_10.623	rel_abund_log2	b1	b3	24	24	3.421006	23	0.0023400	**
897.6305_9.024	rel_abund_log2	b1	b3	24	24	3.843371	23	0.0008290	***
905.6028_9.99	rel_abund_log2	b1	b3	24	24	-2.338062	23	0.0284000	*
905.6039_10.298	rel_abund_log2	b1	b3	24	24	3.261735	23	0.0034300	**
906.5531_8.016	rel_abund_log2	b1	b3	24	24	-2.943948	23	0.0072900	**
906.5828_8.614	rel_abund_log2	b1	b3	24	24	-2.892792	23	0.0082100	**
907.6211_9.035	rel_abund_log2	b1	b3	24	24	-2.797835	23	0.0102000	*
908.5688_8.459	rel_abund_log2	b1	b3	24	24	-2.561187	23	0.0175000	*
908.6381_10.644	rel_abund_log2	b1	b3	24	24	2.512136	23	0.0195000	*
909.6366_10.295	rel_abund_log2	b1	b3	24	24	3.832321	23	0.0008520	***
909.6368_9.99	rel_abund_log2	b1	b3	24	24	-4.206949	23	0.0003360	***
911.6461_9.397	rel_abund_log2	b1	b3	24	24	-4.245416	23	0.0003050	***
913.6162_9.035	rel_abund_log2	b1	b3	24	24	-3.083764	23	0.0052500	**
913.6613_10.438	rel_abund_log2	b1	b3	24	24	-2.396641	23	0.0251000	*
914.5643_8.457	rel_abund_log2	b1	b3	24	24	-2.239444	23	0.0351000	*
915.6319_9.987	rel_abund_log2	b1	b3	24	24	-2.692394	23	0.0130000	*
915.6327_10.293	rel_abund_log2	b1	b3	24	24	3.977629	23	0.0005950	***
917.5422_7.049	rel_abund_log2	b1	b3	24	24	2.645195	23	0.0145000	*
917.6329_9.989	rel_abund_log2	b1	b3	24	24	-3.344524	23	0.0028100	**
917.633_10.292	rel_abund_log2	b1	b3	24	24	2.352180	23	0.0276000	*
917.6482_10.589	rel_abund_log2	b1	b3	24	24	2.952557	23	0.0071400	**
918.5466_6.408	rel_abund_log2	b1	b3	24	24	-2.883008	23	0.0084000	**
918.5543_7.768	rel_abund_log2	b1	b3	24	24	4.396768	23	0.0002090	***
918.5546_7.77	rel_abund_log2	b1	b3	24	24	3.168979	23	0.0042800	**
919.2277_0.616	rel_abund_log2	b1	b3	24	24	4.057019	23	0.0004880	***
919.6483_10.59	rel_abund_log2	b1	b3	24	24	2.139708	23	0.0432000	*
920.6735_10.31	rel_abund_log2	b1	b3	24	24	-6.052016	23	0.0000036	****
922.5024_7.08	rel_abund_log2	b1	b3	24	24	2.287672	23	0.0317000	*
922.5774_7.695	rel_abund_log2	b1	b3	24	24	-3.437800	23	0.0022400	**
922.5781_8.016	rel_abund_log2	b1	b3	24	24	-3.552545	23	0.0017000	**
923.646_9.035	rel_abund_log2	b1	b3	24	24	-4.202579	23	0.0003400	***
923.6524_10.353	rel_abund_log2	b1	b3	24	24	-6.423635	23	0.0000015	****
924.5935_8.459	rel_abund_log2	b1	b3	24	24	-2.324461	23	0.0293000	*
925.6617_9.988	rel_abund_log2	b1	b3	24	24	-4.309254	23	0.0002600	***
927.677_10.59	rel_abund_log2	b1	b3	24	24	3.910058	23	0.0007030	***
93.0344_0.646	rel_abund_log2	b1	b3	24	24	3.075428	23	0.0053500	**
930.5504_7.815	rel_abund_log2	b1	b3	24	24	-3.587048	23	0.0015600	**
931.5514_9.582	rel_abund_log2	b1	b3	24	24	7.026738	23	0.0000004	****
931.6162_9.992	rel_abund_log2	b1	b3	24	24	-2.384761	23	0.0257000	*
931.6162_9.999	rel_abund_log2	b1	b3	24	24	-3.013496	23	0.0061900	**
933.5855_8.781	rel_abund_log2	b1	b3	24	24	3.177299	23	0.0042000	**
934.5326_7.772	rel_abund_log2	b1	b3	24	24	3.266415	23	0.0033900	**
936.5439_7.803	rel_abund_log2	b1	b3	24	24	-2.627182	23	0.0151000	*
938.5513_8.018	rel_abund_log2	b1	b3	24	24	-2.166164	23	0.0409000	*
939.6769_10.335	rel_abund_log2	b1	b3	24	24	-6.354445	23	0.0000018	****
940.5496_7.764	rel_abund_log2	b1	b3	24	24	3.822051	23	0.0008740	***
942.5548_7.339	rel_abund_log2	b1	b3	24	24	-2.355364	23	0.0274000	*
942.5549_7.193	rel_abund_log2	b1	b3	24	24	2.155616	23	0.0418000	*
945.5095_6.552	rel_abund_log2	b1	b3	24	24	2.735307	23	0.0118000	*
945.6385_9.975	rel_abund_log2	b1	b3	24	24	4.917278	23	0.0000573	****
946.5851_8.939	rel_abund_log2	b1	b3	24	24	2.554640	23	0.0177000	*
948.5172_7.192	rel_abund_log2	b1	b3	24	24	2.677992	23	0.0134000	*
950.6082_8.859	rel_abund_log2	b1	b3	24	24	-4.064704	23	0.0004790	***
958.5324_7.191	rel_abund_log2	b1	b3	24	24	3.979074	23	0.0005930	***
959.6018_9.026	rel_abund_log2	b1	b3	24	24	3.695640	23	0.0011900	**
960.7412_11.038	rel_abund_log2	b1	b3	24	24	-4.385688	23	0.0002150	***
961.6178_9.977	rel_abund_log2	b1	b3	24	24	3.383217	23	0.0025600	**
964.5495_7.339	rel_abund_log2	b1	b3	24	24	-2.276145	23	0.0325000	*
967.6322_9.976	rel_abund_log2	b1	b3	24	24	4.411283	23	0.0002020	***
968.5707_8.019	rel_abund_log2	b1	b3	24	24	-2.350355	23	0.0277000	*
968.581_8.937	rel_abund_log2	b1	b3	24	24	3.835836	23	0.0008450	***
969.6384_9.033	rel_abund_log2	b1	b3	24	24	-2.549003	23	0.0179000	*
970.5858_8.459	rel_abund_log2	b1	b3	24	24	-2.554209	23	0.0177000	*
970.6153_10.616	rel_abund_log2	b1	b3	24	24	-2.201530	23	0.0380000	*
971.6542_9.988	rel_abund_log2	b1	b3	24	24	-4.184361	23	0.0003560	***
976.5477_8.457	rel_abund_log2	b1	b3	24	24	-3.382561	23	0.0025600	**
983.6184_9.976	rel_abund_log2	b1	b3	24	24	-4.397506	23	0.0002090	***
983.6185_9.993	rel_abund_log2	b1	b3	24	24	-3.842628	23	0.0008310	***
985.616_9.034	rel_abund_log2	b1	b3	24	24	-3.264112	23	0.0034100	**
986.5637_8.459	rel_abund_log2	b1	b3	24	24	-2.528186	23	0.0188000	*
987.6318_9.987	rel_abund_log2	b1	b3	24	24	-3.493884	23	0.0019600	**
991.6331_9.034	rel_abund_log2	b1	b3	24	24	-2.357923	23	0.0273000	*
992.5806_8.459	rel_abund_log2	b1	b3	24	24	-3.090919	23	0.0051600	**
993.6487_10.29	rel_abund_log2	b1	b3	24	24	3.469618	23	0.0020800	**
993.6488_9.986	rel_abund_log2	b1	b3	24	24	-4.059540	23	0.0004850	***

# how many are significant?
nrow(tomato_t.test_paired_sig)

## [1] 388

Remove BG diet

# let's grab both datasets for features sig in pre v post red and pre v post control and combine them
fulljoin_t.test_tomANDctrl <- full_join(tomato_t.test_paired_sig, ctrl_t.test_paired_sig,
                                        by = "mz_rt",
                                        suffix = c(".tom", ".ctrl"))

# number features in full list
nrow(fulljoin_t.test_tomANDctrl)

## [1] 441

Let’s try and remove features significant due to the background diet

sig_paired_tom_rmBG <- fulljoin_t.test_tomANDctrl %>%
  # add a column to account for direction
  mutate(sign = statistic.tom * statistic.ctrl) %>%
  # replace NAs in the sign column with 0
  mutate(sign = replace_na(sign, 0)) %>%
  # replace NAs in the statistic.ctrl column to 0
  mutate(statistic.ctrl = replace_na(statistic.ctrl, 0)) %>%
  # filter for columns that are either negative (means ctrl and tomato are going in opposite dir) or where the stat.ctrl col is 0 (so we don't remove features that are just not present in the sig control list)
  filter((sign < 0 | statistic.ctrl == 0))

# number of features in new list without bg diet effect
nrow(sig_paired_tom_rmBG)

## [1] 219

How many BG-diet-related features did we remove from the list of significant beta features?

nrow(tomato_t.test_paired_sig) - nrow(sig_paired_tom_rmBG)

## [1] 169

ANOVA overlap

Keep sig features in t-test that have a match in sig ANOVA. Let’s take our new feature list (background diet effect removed)

# select only features from paired list that have a match in ANOVA list
tom_sig_ANOVA_overlap_paired <- inner_join(sig_paired_tom_rmBG,
                                            trt_tukeyHSD_sig,
                                            by = "mz_rt",
                                            suffix = c(".t-test", ".tukeys"))

# features overlapping with sig ANOVA
unique(tom_sig_ANOVA_overlap_paired$mz_rt)

##  [1] "1002.5196_7.769" "1561.2028_9.038" "244.908_0.706"   "526.3144_2.683" 
##  [5] "560.2269_6.891"  "674.6039_10.834" "682.6332_10.835" "705.5178_5.308" 
##  [9] "721.549_6"       "736.5275_8.432"  "764.5798_8.343"  "800.5357_7.469" 
## [13] "824.5771_8.938"  "847.6455_10.298" "848.5114_9.046"  "853.619_10.659" 
## [17] "857.6749_9.986"  "862.5335_7.761"  "863.5644_9.582"  "868.5303_7.476" 
## [21] "887.5991_9.03"   "905.6028_9.99"   "907.6211_9.035"  "909.6368_9.99"  
## [25] "915.6327_10.293" "917.6329_9.989"  "940.5496_7.764"  "945.6385_9.975" 
## [29] "969.6384_9.033"  "983.6184_9.976"  "983.6185_9.993"  "987.6318_9.987"

Mummichog list

paired_tomato_for_mummichog <- tomato_t.test_paired %>%
  dplyr::select(mz_rt,
         p,
         statistic) %>%
  separate(col = mz_rt,
           into = c("m/z", "rt"),
           sep = "_") %>%
  rename("p-value" = "p") %>%
  rename("t-score" = "statistic")

write_csv(paired_red_for_mummichog,
          "for mummichog analysis/t-test-res-tomato-paired.csv")

Here, I will compare control to each tomato treatment individually, and then tomato treatments against each other. I will also compare tomato to control. I am using the log transformed values of rel abundance since parametric tests assume normality.

Red v ctrl

# run t-test
red_v_ctrl_t.test <- df_for_stats %>%
  filter(treatment %in% c("control", "red")) %>%
  filter(period == "b3") %>%
  dplyr::select(subject, treatment, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ treatment, 
         paired = FALSE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()

Statistically significant features

# which features are significant?
sig_red_v_ctrl_t.test <- red_v_ctrl_t.test %>%
  filter(p < 0.05)
kable(sig_red_v_ctrl_t.test)

mz_rt	.y.	group1	group2	n1	n2	statistic	df	p	p.signif
151.0261_0.612	rel_abund_log2	control	red	11	12	2.338023	16.36047	0.03240	*
244.908_0.706	rel_abund_log2	control	red	11	12	-3.452634	20.96521	0.00239	**
362.9406_0.501	rel_abund_log2	control	red	11	12	2.415061	15.52626	0.02850	*
605.4051_5.422	rel_abund_log2	control	red	11	12	-2.400517	20.64957	0.02590	*
705.5178_5.308	rel_abund_log2	control	red	11	12	-2.121762	20.23735	0.04640	*
711.5035_5.685	rel_abund_log2	control	red	11	12	-2.513206	19.15979	0.02110	*
737.5362_6.543	rel_abund_log2	control	red	11	12	2.409275	20.99867	0.02520	*
860.6369_10.19	rel_abund_log2	control	red	11	12	-2.107560	20.55172	0.04750	*
877.5224_6.543	rel_abund_log2	control	red	11	12	2.370644	20.53763	0.02760	*
880.6062_8.116	rel_abund_log2	control	red	11	12	2.088743	20.83942	0.04920	*
890.5493_8.144	rel_abund_log2	control	red	11	12	-2.742924	20.97983	0.01220	*
895.6025_10.452	rel_abund_log2	control	red	11	12	-2.654041	20.99974	0.01480	*
895.6211_9.665	rel_abund_log2	control	red	11	12	2.163212	20.79662	0.04230	*
929.5358_8.639	rel_abund_log2	control	red	11	12	-2.233441	20.71595	0.03670	*
931.6164_10.288	rel_abund_log2	control	red	11	12	2.453375	20.32427	0.02330	*
945.5095_6.552	rel_abund_log2	control	red	11	12	2.145392	16.42973	0.04720	*
945.6385_9.975	rel_abund_log2	control	red	11	12	2.527456	20.03467	0.02000	*

# how many are significant?
nrow(sig_red_v_ctrl_t.test)

## [1] 17

ANOVA overlap

Keep sig features in unpaired t-test that have a match in sig ANOVA

sig_overlap_ctrl_red <- inner_join(sig_red_v_ctrl_t.test,
                                   trt_tukeyHSD_sig,
                                   by = "mz_rt",
                                   suffix = c(".t-test", ".tukeys"))

unique(sig_overlap_ctrl_red$mz_rt)

## [1] "244.908_0.706"  "705.5178_5.308" "711.5035_5.685" "890.5493_8.144"
## [5] "895.6211_9.665" "945.6385_9.975"

Mummichog list

ctrl_v_red_for_mummichog <- red_v_ctrl_t.test %>%
  dplyr::select(mz_rt,
         p,
         statistic) %>%
  separate(col = mz_rt,
           into = c("m/z", "rt"),
           sep = "_") %>%
  rename("p-value" = "p") %>%
  rename("t-score" = "statistic")

write_csv(ctrl_v_red_for_mummichog,
          "for mummichog analysis/t-test-res-ctrl-v-red.csv")

Beta v ctrl

# run t-tests
beta_v_ctrl_t.test <- df_for_stats %>%
  filter(treatment %in% c("control" , "beta"),
         period == "b3") %>%
  dplyr::select(subject, treatment, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ treatment, 
         paired = FALSE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()

Statistically significant features

# which features are significant?
sig_beta_v_ctrl_t.test <- beta_v_ctrl_t.test %>%
  filter(p < 0.05)
kable(sig_beta_v_ctrl_t.test)

mz_rt	.y.	group1	group2	n1	n2	statistic	df	p	p.signif
1271.7465_5.862	rel_abund_log2	beta	control	12	11	-2.602354	16.99728	0.01860	*
1440.1025_6.541	rel_abund_log2	beta	control	12	11	-2.498830	19.70687	0.02140	*
1450.1318_6.54	rel_abund_log2	beta	control	12	11	-2.261593	17.61030	0.03660	*
1502.0921_6.54	rel_abund_log2	beta	control	12	11	-2.601254	20.35857	0.01690	*
1518.1184_6.541	rel_abund_log2	beta	control	12	11	-2.642154	18.11451	0.01650	*
1612.0825_7.034	rel_abund_log2	beta	control	12	11	-2.578554	20.81114	0.01760	*
1644.3337_9.984	rel_abund_log2	beta	control	12	11	-3.083046	18.75198	0.00619	**
1646.3475_10.439	rel_abund_log2	beta	control	12	11	-2.189729	13.27898	0.04700	*
445.3316_4.28	rel_abund_log2	beta	control	12	11	-2.634209	20.98463	0.01550	*
557.4566_6.309	rel_abund_log2	beta	control	12	11	-2.178572	17.72385	0.04310	*
575.467_4.662	rel_abund_log2	beta	control	12	11	-2.327719	19.80703	0.03060	*
654.31_3.076	rel_abund_log2	beta	control	12	11	2.867323	20.75135	0.00929	**
737.5193_5.846	rel_abund_log2	beta	control	12	11	-2.240287	20.78231	0.03610	*
737.5362_6.543	rel_abund_log2	beta	control	12	11	-2.614394	16.17349	0.01870	*
745.5498_5.846	rel_abund_log2	beta	control	12	11	-2.159582	20.62657	0.04270	*
764.5548_6.543	rel_abund_log2	beta	control	12	11	-3.122747	15.46343	0.00678	**
767.5664_7.562	rel_abund_log2	beta	control	12	11	-2.527314	20.00745	0.02000	*
797.5117_5.847	rel_abund_log2	beta	control	12	11	-2.608931	20.73450	0.01650	*
803.5076_5.847	rel_abund_log2	beta	control	12	11	-2.145926	20.86749	0.04380	*
819.6141_9.024	rel_abund_log2	beta	control	12	11	-2.315440	20.88308	0.03080	*
821.565_7.768	rel_abund_log2	beta	control	12	11	2.410504	20.46941	0.02540	*
821.6152_8.817	rel_abund_log2	beta	control	12	11	-2.242101	20.54913	0.03610	*
829.6432_9.024	rel_abund_log2	beta	control	12	11	-2.109984	20.99862	0.04700	*
833.6297_9.665	rel_abund_log2	beta	control	12	11	-2.412993	20.99947	0.02500	*
843.6587_9.666	rel_abund_log2	beta	control	12	11	-2.531978	20.89053	0.01940	*
847.6455_10.298	rel_abund_log2	beta	control	12	11	-2.409479	20.95110	0.02530	*
853.6428_8.12	rel_abund_log2	beta	control	12	11	-2.356496	20.26352	0.02860	*
855.6591_9.033	rel_abund_log2	beta	control	12	11	-2.187249	18.52780	0.04180	*
857.6746_10.294	rel_abund_log2	beta	control	12	11	-2.186688	20.47124	0.04050	*
869.598_7.834	rel_abund_log2	beta	control	12	11	-2.265410	20.94333	0.03420	*
877.5224_6.543	rel_abund_log2	beta	control	12	11	-2.086665	20.96393	0.04930	*
886.5199_7.016	rel_abund_log2	beta	control	12	11	2.438518	18.87384	0.02480	*
890.5493_8.144	rel_abund_log2	beta	control	12	11	2.103000	20.71634	0.04790	*
895.6211_9.665	rel_abund_log2	beta	control	12	11	-2.483772	20.49984	0.02170	*
896.6737_10.623	rel_abund_log2	beta	control	12	11	-2.117449	19.00880	0.04760	*
903.5878_9.033	rel_abund_log2	beta	control	12	11	-2.870222	19.78432	0.00953	**
904.599_5.836	rel_abund_log2	beta	control	12	11	-2.449384	20.44569	0.02340	*
907.6211_9.035	rel_abund_log2	beta	control	12	11	-2.927436	16.99932	0.00940	**
911.6459_9.666	rel_abund_log2	beta	control	12	11	-2.515074	20.74477	0.02020	*
913.6162_9.035	rel_abund_log2	beta	control	12	11	-2.368208	20.03066	0.02800	*
917.633_10.292	rel_abund_log2	beta	control	12	11	-2.170774	20.43166	0.04190	*
923.646_9.035	rel_abund_log2	beta	control	12	11	-2.772763	20.76640	0.01150	*
931.6164_10.288	rel_abund_log2	beta	control	12	11	-2.372446	19.13541	0.02830	*
945.6385_9.975	rel_abund_log2	beta	control	12	11	-2.665153	19.65665	0.01500	*
950.5333_8.144	rel_abund_log2	beta	control	12	11	2.392149	17.30117	0.02840	*
967.6322_9.976	rel_abund_log2	beta	control	12	11	-2.774084	19.40881	0.01190	*
969.6092_10.587	rel_abund_log2	beta	control	12	11	2.401877	20.33160	0.02600	*
985.616_9.034	rel_abund_log2	beta	control	12	11	-2.247727	20.07430	0.03600	*
991.6331_9.034	rel_abund_log2	beta	control	12	11	-2.236592	18.15637	0.03810	*

# how many are significant?
nrow(sig_beta_v_ctrl_t.test)

## [1] 49

ANOVA overlap

Keep sig features in t-test that have a match in sig ANOVA

sig_overlap_ctrl_beta <- inner_join(sig_beta_v_ctrl_t.test,
                                   trt_tukeyHSD_sig,
                                   by = "mz_rt",
                                   suffix = c(".t-test", ".tukeys"))

# which features overlap?
unique(sig_overlap_ctrl_beta$mz_rt)

##  [1] "1271.7465_5.862"  "1646.3475_10.439" "557.4566_6.309"   "575.467_4.662"   
##  [5] "764.5548_6.543"   "767.5664_7.562"   "797.5117_5.847"   "819.6141_9.024"  
##  [9] "829.6432_9.024"   "833.6297_9.665"   "843.6587_9.666"   "847.6455_10.298" 
## [13] "853.6428_8.12"    "855.6591_9.033"   "857.6746_10.294"  "869.598_7.834"   
## [17] "886.5199_7.016"   "890.5493_8.144"   "895.6211_9.665"   "903.5878_9.033"  
## [21] "907.6211_9.035"   "911.6459_9.666"   "913.6162_9.035"   "923.646_9.035"   
## [25] "945.6385_9.975"   "985.616_9.034"

Mummichog list

ctrl_v_beta_for_mummichog <- beta_v_ctrl_t.test %>%
  dplyr::select(mz_rt,
         p,
         statistic) %>%
  separate(col = mz_rt,
           into = c("m/z", "rt"),
           sep = "_") %>%
  rename("p-value" = "p") %>%
  rename("t-score" = "statistic")

write_csv(ctrl_v_beta_for_mummichog,
          "for mummichog analysis/t-test-res-ctrl-v-beta.csv")

Beta v red

# run t-tests
beta_v_red_t.test <- df_for_stats %>%
  filter(treatment %in% c("beta", "red"),
         period == "b3") %>%
  dplyr::select(subject, treatment, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ treatment, 
         paired = FALSE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()

Statistically significant features

# which features are significant?
sig_beta_v_red_t.test <- beta_v_red_t.test %>%
  filter(p < 0.05)
kable(sig_beta_v_red_t.test)

mz_rt	.y.	group1	group2	n1	n2	statistic	df	p	p.signif
244.908_0.706	rel_abund_log2	beta	red	12	12	-2.099288	21.52667	0.04780	*
297.1527_2.281	rel_abund_log2	beta	red	12	12	2.770830	21.98423	0.01120	*
311.1683_2.682	rel_abund_log2	beta	red	12	12	2.669852	19.44820	0.01490	*
362.9406_0.501	rel_abund_log2	beta	red	12	12	3.560133	18.97481	0.00209	**
473.3629_4.632	rel_abund_log2	beta	red	12	12	-2.397707	21.54749	0.02560	*
532.2989_3.075	rel_abund_log2	beta	red	12	12	2.946249	16.22111	0.00938	**
557.4566_6.309	rel_abund_log2	beta	red	12	12	-2.889624	19.50467	0.00922	**
559.4719_6.867	rel_abund_log2	beta	red	12	12	-2.371316	18.25286	0.02890	*
591.462_4.314	rel_abund_log2	beta	red	12	12	-2.419627	21.99982	0.02420	*
654.31_3.076	rel_abund_log2	beta	red	12	12	2.679382	21.32549	0.01390	*
711.5035_5.685	rel_abund_log2	beta	red	12	12	-2.076854	21.94549	0.04970	*
797.5117_5.847	rel_abund_log2	beta	red	12	12	-2.196075	21.92973	0.03900	*
818.6271_10.414	rel_abund_log2	beta	red	12	12	-2.330254	21.99011	0.02940	*
820.5155_3.079	rel_abund_log2	beta	red	12	12	3.050123	19.73669	0.00639	**
830.5005_2.723	rel_abund_log2	beta	red	12	12	2.198696	15.31911	0.04370	*
840.6112_8.445	rel_abund_log2	beta	red	12	12	-2.925405	19.19274	0.00862	**
849.4911_5.685	rel_abund_log2	beta	red	12	12	-2.299673	21.95091	0.03140	*
853.619_10.659	rel_abund_log2	beta	red	12	12	-2.104137	21.98626	0.04700	*
860.6369_10.19	rel_abund_log2	beta	red	12	12	-2.137842	21.45621	0.04420	*
878.584_5.681	rel_abund_log2	beta	red	12	12	-2.223055	21.24129	0.03720	*
903.5878_9.033	rel_abund_log2	beta	red	12	12	-3.393211	18.39625	0.00316	**

# how many are significant?
nrow(sig_beta_v_red_t.test)

## [1] 21

ANOVA overlap

Keep sig features in t-test that have a match in sig ANOVA

sig_overlap_beta_red <- inner_join(sig_beta_v_red_t.test,
                                   trt_tukeyHSD_sig,
                                   by = "mz_rt",
                                   suffix = c(".t-test", ".tukeys"))

# which features overlap?
unique(sig_overlap_beta_red$mz_rt)

## [1] "244.908_0.706"   "557.4566_6.309"  "559.4719_6.867"  "591.462_4.314"  
## [5] "711.5035_5.685"  "797.5117_5.847"  "818.6271_10.414" "853.619_10.659" 
## [9] "903.5878_9.033"

Mummichog list

beta_v_red_for_mummichog <- beta_v_red_t.test %>%
  dplyr::select(mz_rt,
         p,
         statistic) %>%
  separate(col = mz_rt,
           into = c("m/z", "rt"),
           sep = "_") %>%
  rename("p-value" = "p") %>%
  rename("t-score" = "statistic")

write_csv(beta_v_red_for_mummichog,
          "for mummichog analysis/t-test-res-beta-v-red.csv")

Tomato v ctrl

# run t-tests
tom_v_ctrl_t.test <- df_for_stats %>%
  filter(tomato_or_control %in% c("control", "tomato"),
         period == "b3") %>%
  dplyr::select(subject, tomato_or_control, mz_rt, rel_abund_log2) %>%
  group_by(mz_rt) %>%
  t_test(rel_abund_log2 ~ tomato_or_control, 
         paired = FALSE, 
         p.adjust.method = "BH") %>% # Benjamini-Hochberg controlling to lower false positives
  add_significance()

Statistically significant features

# which features are significant?
sig_tom_v_ctrl_t.test <- tom_v_ctrl_t.test %>%
  filter(p < 0.05)
kable(sig_tom_v_ctrl_t.test)

mz_rt	.y.	group1	group2	n1	n2	statistic	df	p	p.signif
1440.1025_6.541	rel_abund_log2	control	tomato	11	24	2.697449	26.30317	0.01200	*
1450.1318_6.54	rel_abund_log2	control	tomato	11	24	2.162725	28.17691	0.03920	*
1502.0921_6.54	rel_abund_log2	control	tomato	11	24	2.254239	24.47013	0.03340	*
1518.1184_6.541	rel_abund_log2	control	tomato	11	24	2.559290	29.02690	0.01600	*
1644.3337_9.984	rel_abund_log2	control	tomato	11	24	3.122548	27.03693	0.00424	**
1646.3475_10.439	rel_abund_log2	control	tomato	11	24	2.107761	32.56337	0.04280	*
244.908_0.706	rel_abund_log2	control	tomato	11	24	-2.849128	22.07836	0.00931	**
526.3144_2.683	rel_abund_log2	control	tomato	11	24	-2.114205	24.24561	0.04500	*
605.4051_5.422	rel_abund_log2	control	tomato	11	24	-2.448664	30.43772	0.02030	*
737.5362_6.543	rel_abund_log2	control	tomato	11	24	3.093542	30.22575	0.00423	**
745.5498_5.846	rel_abund_log2	control	tomato	11	24	2.072676	22.54078	0.04980	*
764.5548_6.543	rel_abund_log2	control	tomato	11	24	2.799299	32.96766	0.00849	**
767.5664_7.562	rel_abund_log2	control	tomato	11	24	2.470170	25.78700	0.02040	*
775.5965_7.563	rel_abund_log2	control	tomato	11	24	2.107058	31.71271	0.04310	*
788.5437_6.549	rel_abund_log2	control	tomato	11	24	-2.387040	30.29985	0.02340	*
803.5076_5.847	rel_abund_log2	control	tomato	11	24	2.128987	19.93580	0.04590	*
819.6141_9.024	rel_abund_log2	control	tomato	11	24	2.335152	20.56272	0.02980	*
821.565_7.768	rel_abund_log2	control	tomato	11	24	-2.514823	17.29324	0.02210	*
821.6139_9.026	rel_abund_log2	control	tomato	11	24	2.170798	18.09512	0.04350	*
829.6432_9.024	rel_abund_log2	control	tomato	11	24	2.198822	20.17973	0.03970	*
833.6297_9.665	rel_abund_log2	control	tomato	11	24	2.494656	23.32964	0.02010	*
835.6457_10.444	rel_abund_log2	control	tomato	11	24	2.294734	32.89860	0.02830	*
843.6587_9.666	rel_abund_log2	control	tomato	11	24	2.165548	23.80584	0.04060	*
847.6455_10.298	rel_abund_log2	control	tomato	11	24	2.492918	25.05938	0.01960	*
855.6591_9.033	rel_abund_log2	control	tomato	11	24	2.389511	26.77448	0.02420	*
857.6746_10.294	rel_abund_log2	control	tomato	11	24	2.410083	26.40524	0.02320	*
869.598_7.834	rel_abund_log2	control	tomato	11	24	2.111475	19.29221	0.04800	*
877.5224_6.543	rel_abund_log2	control	tomato	11	24	2.644829	22.23392	0.01470	*
886.5199_7.016	rel_abund_log2	control	tomato	11	24	-2.643888	27.67046	0.01330	*
890.5493_8.144	rel_abund_log2	control	tomato	11	24	-2.821312	20.34803	0.01040	*
895.6211_9.665	rel_abund_log2	control	tomato	11	24	2.790066	23.61123	0.01030	*
907.6211_9.035	rel_abund_log2	control	tomato	11	24	2.873048	30.43628	0.00734	**
911.6459_9.666	rel_abund_log2	control	tomato	11	24	2.199363	21.02825	0.03920	*
913.6162_9.035	rel_abund_log2	control	tomato	11	24	2.421647	22.29324	0.02400	*
915.6327_10.293	rel_abund_log2	control	tomato	11	24	2.394575	31.08177	0.02290	*
923.646_9.035	rel_abund_log2	control	tomato	11	24	2.668043	21.54311	0.01420	*
924.518_7.766	rel_abund_log2	control	tomato	11	24	-2.080283	26.40791	0.04730	*
931.6164_10.288	rel_abund_log2	control	tomato	11	24	2.962534	26.56891	0.00636	**
945.5095_6.552	rel_abund_log2	control	tomato	11	24	2.716900	32.63198	0.01050	*
945.6385_9.975	rel_abund_log2	control	tomato	11	24	3.060858	22.80094	0.00557	**
950.5333_8.144	rel_abund_log2	control	tomato	11	24	-2.051392	29.80883	0.04910	*
967.6322_9.976	rel_abund_log2	control	tomato	11	24	2.919161	26.49412	0.00708	**
969.6092_10.587	rel_abund_log2	control	tomato	11	24	-2.231956	21.04607	0.03660	*
971.6542_9.988	rel_abund_log2	control	tomato	11	24	2.179708	20.40680	0.04120	*
985.616_9.034	rel_abund_log2	control	tomato	11	24	2.495541	24.00961	0.01980	*
991.6331_9.034	rel_abund_log2	control	tomato	11	24	2.050243	29.21562	0.04940	*

# how many are significant?
nrow(sig_tom_v_ctrl_t.test)

## [1] 46

ANOVA overlap

Keep sig features in t-test that have a match in sig ANOVA

sig_overlap_tom_ctrl <- inner_join(sig_tom_v_ctrl_t.test,
                                   trt_tukeyHSD_sig,
                                   by = "mz_rt",
                                   suffix = c(".t-test", ".tukeys"))

# which features overlap?
unique(sig_overlap_tom_ctrl$mz_rt)

##  [1] "1646.3475_10.439" "244.908_0.706"    "526.3144_2.683"   "764.5548_6.543"  
##  [5] "767.5664_7.562"   "819.6141_9.024"   "821.6139_9.026"   "829.6432_9.024"  
##  [9] "833.6297_9.665"   "835.6457_10.444"  "843.6587_9.666"   "847.6455_10.298" 
## [13] "855.6591_9.033"   "857.6746_10.294"  "869.598_7.834"    "886.5199_7.016"  
## [17] "890.5493_8.144"   "895.6211_9.665"   "907.6211_9.035"   "911.6459_9.666"  
## [21] "913.6162_9.035"   "915.6327_10.293"  "923.646_9.035"    "924.518_7.766"   
## [25] "945.6385_9.975"   "971.6542_9.988"   "985.616_9.034"

Mummichog list

tom_v_ctrl_for_mummichog <- tom_v_ctrl_t.test %>%
  dplyr::select(mz_rt,
         p,
         statistic) %>%
  separate(col = mz_rt,
           into = c("m/z", "rt"),
           sep = "_") %>%
  rename("p-value" = "p") %>%
  rename("t-score" = "statistic")

write_csv(tom_v_ctrl_for_mummichog,
          "for mummichog analysis/t-test-res-ctrl-v-tomato.csv")

Heatmaps

ANOVA

# go back to wide for stats df
df_for_stats_wide <- df_for_stats %>%
  pivot_wider(names_from = mz_rt,
              values_from = rel_abund_log2)

ANOVA_trtperiod_heatmap_data <- df_for_stats_wide %>%
  filter(period == "b3") %>%
  dplyr::select(sample, 
                all_of(trt_anova_sig$mz_rt)) %>%
  column_to_rownames("sample")

head(ANOVA_trtperiod_heatmap_data, n=3)

##                         1334.2417_10.855 1335.245_10.854 1554.1226_7.773
## x5107_b3_beta_c18neg_28         11.89168        11.93301        13.11223
## x5109_b3_beta_c18neg_81         11.35384        11.39136        12.35018
## x5112_b3_beta_c18neg_56         11.66017        11.60905        12.46035
##                         1561.2028_9.038 201.0226_0.688 244.908_0.706
## x5107_b3_beta_c18neg_28        12.38974       8.987843      14.28148
## x5109_b3_beta_c18neg_81        11.33230      10.583197      14.24202
## x5112_b3_beta_c18neg_56        12.17100      10.430688      14.90995
##                         352.0856_0.687 437.0542_0.688 462.1763_0.655
## x5107_b3_beta_c18neg_28       12.19208       11.59417       12.12562
## x5109_b3_beta_c18neg_81       13.44411       13.69906       13.46653
## x5112_b3_beta_c18neg_56       12.33612       12.14682       12.06409
##                         507.223_1.7 507.223_2.273 551.3582_4.841 557.457_6.865
## x5107_b3_beta_c18neg_28    15.82112      13.36222       15.04541      11.88666
## x5109_b3_beta_c18neg_81    16.10839      13.94255       14.21245      11.49448
## x5112_b3_beta_c18neg_56    15.89406      13.64799       14.45460      11.97055
##                         559.4719_6.867 577.3738_4.918 579.3893_5.557
## x5107_b3_beta_c18neg_28       11.90796       13.87389       14.01704
## x5109_b3_beta_c18neg_81       11.98511       13.66888       13.95120
## x5112_b3_beta_c18neg_56       12.29566       13.54795       13.40897
##                         591.3894_5.174 591.462_4.314 591.462_4.651
## x5107_b3_beta_c18neg_28       14.09410      12.10241      11.67833
## x5109_b3_beta_c18neg_81       13.69385      12.19859      11.83841
## x5112_b3_beta_c18neg_56       13.98307      12.07909      11.69268
##                         684.6065_10.853 794.5464_7.772 818.6271_10.414
## x5107_b3_beta_c18neg_28        18.13677       16.37619        14.97830
## x5109_b3_beta_c18neg_81        17.86967       15.88000        14.50881
## x5112_b3_beta_c18neg_56        18.15119       16.18453        14.68936
##                         819.6141_9.024 821.6139_9.026 827.6272_7.905
## x5107_b3_beta_c18neg_28       14.35328       13.00312       14.11301
## x5109_b3_beta_c18neg_81       13.86363       12.69708       13.59230
## x5112_b3_beta_c18neg_56       14.10297       12.95969       14.13292
##                         829.6432_9.024 831.6576_9.28 841.6406_8.452
## x5107_b3_beta_c18neg_28       16.79052      12.46298       12.79270
## x5109_b3_beta_c18neg_81       16.20519      12.29325       12.47057
## x5112_b3_beta_c18neg_56       16.42180      12.76196       12.78832
##                         843.6586_9.396 853.6428_8.12 857.6737_9.513
## x5107_b3_beta_c18neg_28       14.98839      13.31049       11.91099
## x5109_b3_beta_c18neg_81       14.43325      13.54001       11.49423
## x5112_b3_beta_c18neg_56       14.96732      13.58702       12.29627
##                         863.5644_9.582 871.6891_10.337 878.5903_7.305
## x5107_b3_beta_c18neg_28       14.15278        14.94652       13.50403
## x5109_b3_beta_c18neg_81       13.57718        14.86260       13.29179
## x5112_b3_beta_c18neg_56       13.52384        15.42354       13.60690
##                         881.6055_9.023 883.6211_9.976 887.5991_9.03
## x5107_b3_beta_c18neg_28       13.27430       13.67873      12.98071
## x5109_b3_beta_c18neg_81       12.90949       13.42718      12.51242
## x5112_b3_beta_c18neg_56       12.93948       13.42897      12.94346
##                         931.5514_9.582 939.6769_10.335
## x5107_b3_beta_c18neg_28       11.86885        12.68854
## x5109_b3_beta_c18neg_81       11.07676        12.44487
## x5112_b3_beta_c18neg_56       11.36730        13.06843

# pull metadata
metadata_Heatmap <- metadata

# change treatment to factor
metadata_Heatmap$treatment <- as.factor(metadata_Heatmap$treatment)

# make it so that rownames in metadata match rownames from heatmap df
rownames(metadata_Heatmap) <- rownames(ANOVA_trtperiod_heatmap_data)

# create annotation rows for treatment and wrangle
# select rownames (samples) from heatmap metadata (also ensures the order is correct)
anno_trt_row <- as.data.frame(rownames(metadata_Heatmap))

# pull trt column
anno_trt_row$treatment <- metadata_Heatmap$treatment
anno_trt_row$sex <- metadata_Heatmap$sex

# select trt
anno_trt_row <- anno_trt_row %>%
  dplyr::select(treatment, sex)

# get rownames to match heatmap again
rownames(anno_trt_row) <- rownames(metadata_Heatmap)

# create annotation colors
annotation_colors <- list(treatment = c("beta" = "orange",
                                        "control" = "green",
                                        "red" = "tomato"),
                          sex = c("M" = "burlywood",
                                  "F" = "pink"))

pheatmap(t(ANOVA_trtperiod_heatmap_data),
           scale = "row",
           cluster_rows = TRUE,
           annotation_col = anno_trt_row,
           annotation_colors = annotation_colors,
           cluster_cols = TRUE,
           show_colnames = TRUE,
           clustering_distance_rows = "euclidean",
           clustering_distance_cols = "euclidean",
           cutree_rows = 8,
           cutree_cols = 3,
           clustering_method = "ward.D2",
           color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
           main = "Heatmap of features significant in ANOVA across all treatment groups \nby Benjamoni-Hochberg corrected \np-values > 0.05 \nLipidomics C18 (-) \nShowing only post-intervention timepoints") 

# Without hierarchical clustering of samples (cols)
pheatmap(t(ANOVA_trtperiod_heatmap_data),
           scale = "row",
           cluster_rows = TRUE,
           annotation_col = anno_trt_row,
           annotation_colors = annotation_colors,
           cluster_cols = FALSE,
           show_colnames = TRUE,
           clustering_distance_rows = "euclidean",
           clustering_distance_cols = "euclidean",
           cutree_rows = 8,
           cutree_cols = 3,
           clustering_method = "ward.D2",
           color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
           main = "Heatmap of features significant in ANOVA across treatment groups \nby Benjamoni-Hochberg corrected \np-values > 0.05 \nLipidomics C18 (-) \nShowing only post-intervention timepoints") 

Unpaired

unpaired_t.tests_heatmap_data <- df_for_stats_wide %>%
  filter(period == "b3") %>%
  dplyr::select(sample, 
                all_of(sig_overlap_ctrl_beta$mz_rt),
                all_of(sig_overlap_ctrl_red$mz_rt),
                all_of(sig_overlap_tom_ctrl$mz_rt),
                all_of(sig_overlap_beta_red$mz_rt)) %>%
  column_to_rownames("sample")

head(unpaired_t.tests_heatmap_data,n = 3)

##                         1271.7465_5.862 1646.3475_10.439 557.4566_6.309
## x5107_b3_beta_c18neg_28        13.17066         11.59082       12.53121
## x5109_b3_beta_c18neg_81        12.69443         10.85874       12.40276
## x5112_b3_beta_c18neg_56        12.89604         11.55739       12.92316
##                         575.467_4.662 764.5548_6.543 767.5664_7.562
## x5107_b3_beta_c18neg_28      11.09816       13.46956       13.17776
## x5109_b3_beta_c18neg_81      11.20832       13.17250       12.96589
## x5112_b3_beta_c18neg_56      10.81837       13.59083       12.86548
##                         797.5117_5.847 819.6141_9.024 829.6432_9.024
## x5107_b3_beta_c18neg_28       13.91578       14.35328       16.79052
## x5109_b3_beta_c18neg_81       13.51247       13.86363       16.20519
## x5112_b3_beta_c18neg_56       13.63163       14.10297       16.42180
##                         833.6297_9.665 843.6587_9.666 847.6455_10.298
## x5107_b3_beta_c18neg_28       13.78397       15.71573        15.55911
## x5109_b3_beta_c18neg_81       13.44514       15.19044        15.26548
## x5112_b3_beta_c18neg_56       13.63559       15.45538        15.33875
##                         853.6428_8.12 855.6591_9.033 857.6746_10.294
## x5107_b3_beta_c18neg_28      13.31049       17.38560        17.24798
## x5109_b3_beta_c18neg_81      13.54001       16.73622        16.68291
## x5112_b3_beta_c18neg_56      13.58702       17.22952        16.89029
##                         869.598_7.834 886.5199_7.016 890.5493_8.144
## x5107_b3_beta_c18neg_28      12.79628       12.77818       13.58262
## x5109_b3_beta_c18neg_81      12.62707       13.02092       13.32260
## x5112_b3_beta_c18neg_56      12.19584       12.90366       13.57013
##                         895.6211_9.665 903.5878_9.033 907.6211_9.035
## x5107_b3_beta_c18neg_28       12.76520       12.38543       13.72623
## x5109_b3_beta_c18neg_81       12.22281       12.16643       13.40179
## x5112_b3_beta_c18neg_56       12.78374       12.40267       13.68643
##                         911.6459_9.666 913.6162_9.035 923.646_9.035
## x5107_b3_beta_c18neg_28       13.62686       13.41607      14.85786
## x5109_b3_beta_c18neg_81       13.23490       13.14782      14.44927
## x5112_b3_beta_c18neg_56       13.29647       13.34383      14.79689
##                         945.6385_9.975 985.616_9.034 244.908_0.706
## x5107_b3_beta_c18neg_28       12.33899      13.17591      14.28148
## x5109_b3_beta_c18neg_81       12.12268      12.58536      14.24202
## x5112_b3_beta_c18neg_56       12.11486      13.00376      14.90995
##                         705.5178_5.308 711.5035_5.685 526.3144_2.683
## x5107_b3_beta_c18neg_28       12.77114       13.34210       14.23104
## x5109_b3_beta_c18neg_81       12.15313       12.85181       14.24883
## x5112_b3_beta_c18neg_56       12.91117       13.14473       14.13615
##                         821.6139_9.026 835.6457_10.444 915.6327_10.293
## x5107_b3_beta_c18neg_28       13.00312        16.81563        13.85985
## x5109_b3_beta_c18neg_81       12.69708        16.53929        13.31377
## x5112_b3_beta_c18neg_56       12.95969        16.76968        13.65187
##                         924.518_7.766 971.6542_9.988 559.4719_6.867
## x5107_b3_beta_c18neg_28      12.89559       12.88359       11.90796
## x5109_b3_beta_c18neg_81      13.11896       12.54816       11.98511
## x5112_b3_beta_c18neg_56      13.20034       12.81446       12.29566
##                         591.462_4.314 818.6271_10.414 853.619_10.659
## x5107_b3_beta_c18neg_28      12.10241        14.97830       11.82644
## x5109_b3_beta_c18neg_81      12.19859        14.50881       11.56742
## x5112_b3_beta_c18neg_56      12.07909        14.68936       11.96410

pheatmap(t(unpaired_t.tests_heatmap_data),
           scale = "row",
           cluster_rows = TRUE,
           annotation_col = anno_trt_row,
           annotation_colors = annotation_colors,
           cluster_cols = TRUE,
           show_colnames = TRUE,
           clustering_distance_rows = "euclidean",
           clustering_distance_cols = "euclidean",
         cutree_rows = 8,
         cutree_cols = 3,
           clustering_method = "ward.D2",
           color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
           main = "Heatmap of features significant in all unpaired t-test comparisons that overlap with sig ANOVA features across all treatment groups \nby Benjamoni-Hochberg corrected \np-values > 0.05 \nLipidomics C18 (-)")

# without clustering of cols
pheatmap(t(unpaired_t.tests_heatmap_data),
           scale = "row",
           cluster_rows = TRUE,
           annotation_col = anno_trt_row,
           annotation_colors = annotation_colors,
           cluster_cols = FALSE,
           show_colnames = TRUE,
           clustering_distance_rows = "euclidean",
           clustering_distance_cols = "euclidean",
         cutree_rows = 8,
         cutree_cols = 3,
           clustering_method = "ward.D2",
           color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
           main = "Heatmap of features significant in all unpaired t-test comparisons that overlap with sig ANOVA features across all treatment groups \nby Benjamoni-Hochberg corrected \np-values > 0.05 \nLipidomics C18 (-)")

Paired

Red

red_paired_t.tests_heatmap_data <- df_for_stats_wide %>%
  filter(treatment == "red") %>%
  dplyr::select(sample, period, treatment, sex,
                all_of(red_sig_ANOVA_overlap_paired$mz_rt)) %>%
  mutate_at("period", as.factor) %>%
  column_to_rownames("sample")

# create annotation rows for pre/post interventions and wrangle
# select rownames (samples) from heatmap metadata (also ensures the order is correct)
anno_red_row_paired <- as.data.frame(rownames(red_paired_t.tests_heatmap_data))


# pull period into a column
anno_red_row_paired$period <- red_paired_t.tests_heatmap_data$period
anno_red_row_paired$sex <- red_paired_t.tests_heatmap_data$sex

# select cols
anno_red_row_paired <- anno_red_row_paired %>%
  dplyr::select(period, sex)

# get rownames to match heatmap again
rownames(anno_red_row_paired) <- rownames(red_paired_t.tests_heatmap_data)

# create annotation colors
red_annotation_colors <- list(period = c("b1" = "darksalmon",
                                        "b3" = "red"),
                              sex = c("M" = "aquamarine2",
                                      "F" = "pink"))

pheatmap(t(red_paired_t.tests_heatmap_data[,-c(1:3)]),
           scale = "row",
           cluster_rows = TRUE,
           annotation_col = anno_red_row_paired,
           annotation_colors = red_annotation_colors,
           cluster_cols = TRUE,
           show_rownames = TRUE,
           clustering_distance_rows = "euclidean",
           clustering_distance_cols = "euclidean",
           cutree_rows = 8,
         cutree_cols = 2,
           clustering_method = "ward.D2",
           color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
           main = "Heatmap of significant features pre- vs. post-High-Lyc paired t-test \nsig in ANOVA across all treatment groups \nby Benjamoni-Hochberg corrected p-values > 0.05 \nBackground diet effect removed \nLipidomics C18 (-)")

Beta

beta_paired_t.tests_heatmap_data <- df_for_stats_wide %>%
  filter(treatment == "beta") %>%
  dplyr::select(sample, period, treatment, sex,
                all_of(beta_sig_ANOVA_overlap_paired$mz_rt)) %>%
  mutate_at("period", as.factor) %>%
  column_to_rownames("sample")

# create annotation rows for pre/post interventions and wrangle
# select rownames (samples) from heatmap metadata (also ensures the order is correct)
anno_beta_row_paired <- as.data.frame(rownames(beta_paired_t.tests_heatmap_data))


# pull period into a column
anno_beta_row_paired$period <- beta_paired_t.tests_heatmap_data$period
anno_beta_row_paired$sex <- beta_paired_t.tests_heatmap_data$sex

# select cols
anno_beta_row_paired <- anno_beta_row_paired %>%
  dplyr::select(sex, period)

# get rownames to match heatmap again
rownames(anno_beta_row_paired) <- rownames(beta_paired_t.tests_heatmap_data)

# create annotation colors
beta_annotation_colors <- list(period = c("b1" = "bisque",
                                        "b3" = "darkorange"),
                              sex = c("M" = "aquamarine2",
                                      "F" = "pink"))

pheatmap(t(beta_paired_t.tests_heatmap_data[,-c(1:3)]),
           scale = "row",
           cluster_rows = TRUE,
           annotation_col = anno_beta_row_paired,
           annotation_colors = beta_annotation_colors,
           cluster_cols = TRUE,
           show_rownames = TRUE,
           clustering_distance_rows = "euclidean",
           clustering_distance_cols = "euclidean",
           cutree_rows = 10,
         cutree_cols = 5,
           clustering_method = "ward.D2",
           color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
           main = "Heatmap of significant features pre- vs. post-High-beta paired t-test \nsig in ANOVA across all treatment groups \nby Benjamoni-Hochberg corrected p-values > 0.05 \nBackground diet effect removed \nLipidomics C18 (+)")

Tomato

tom_paired_t.tests_heatmap_data <- df_for_stats_wide %>%
  filter(treatment != "control") %>%
  dplyr::select(sample, period, tomato_or_control, sex,
                all_of(tom_sig_ANOVA_overlap_paired$mz_rt)) %>%
  mutate_at("period", as.factor) %>%
  column_to_rownames("sample")

# create annotation rows for pre/post interventions and wrangle
# select rownames (samples) from heatmap metadata (also ensures the order is correct)
anno_tom_row_paired <- as.data.frame(rownames(tom_paired_t.tests_heatmap_data))


# pull period into a column
anno_tom_row_paired$period <- tom_paired_t.tests_heatmap_data$period
anno_tom_row_paired$sex <- tom_paired_t.tests_heatmap_data$sex

# select cols
anno_tom_row_paired <- anno_tom_row_paired %>%
  dplyr::select(period, sex)

# get rownames to match heatmap again
rownames(anno_tom_row_paired) <- rownames(tom_paired_t.tests_heatmap_data)

# create annotation colors
tom_annotation_colors <- list(period = c("b1" = "darksalmon",
                                        "b3" = "tomato"),
                              sex = c("M" = "aquamarine2",
                                      "F" = "pink"))

pheatmap(t(tom_paired_t.tests_heatmap_data[,-c(1:3)]),
           scale = "row",
           cluster_rows = TRUE,
           annotation_col = anno_tom_row_paired,
           annotation_colors = tom_annotation_colors,
           cluster_cols = TRUE,
           show_rownames = TRUE,
           clustering_distance_rows = "euclidean",
           clustering_distance_cols = "euclidean",
           cutree_rows = 8,
         cutree_cols = 2,
           clustering_method = "ward.D2",
           color = colorRampPalette(c("#67a9cf", "#f7f7f7", "#ef8a62"))(16),
           main = "Heatmap of significant features pre- vs. post-Tomato paired t-test \nsig in ANOVA across all treatment groups \nby Benjamoni-Hochberg corrected p-values > 0.05 \nBackground diet effect removed \nLipidomics C18 (+)")

Joined lists

df_for_stats <- df_for_stats %>%
  # add rel abund levels back since this got lost during drift correction
  mutate(rel_abund = 2^(rel_abund_log2))

Using t tests sig

Tomato effects

How many features are significant (with background diet effect removed)

dim(sig_paired_tom_rmBG)

## [1] 219  20

Which features are significant in unpaired t test comparisons?

# select only significant features from pre v post tomato effect that have a matching key to significant features post tomato v. post control comparison

overall_tomato_effect <- semi_join(sig_paired_tom_rmBG,
                                   sig_tom_v_ctrl_t.test,
                                  by = "mz_rt")

dim(overall_tomato_effect)

## [1]  9 20

Summary stats

(FC_tomato_effect <- df_for_stats %>%
  filter(mz_rt %in% overall_tomato_effect$mz_rt) %>%
  select(subject, treatment_period, mz_rt, rel_abund) %>%
  group_by(mz_rt) %>%
  pivot_wider(names_from = treatment_period,
              values_from = rel_abund) %>%
  mutate(control_FC = control_b3/control_b1,
         beta_FC = beta_b3/beta_b1,
         red_FC = red_b3/red_b1) %>%
  summarize(mean_control_FC = mean(control_FC, na.rm = TRUE),
            mean_beta_FC = mean(beta_FC, na.rm = TRUE),
            mean_red_FC = mean(red_FC, na.rm = TRUE),
            mean_ctrl_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
            mean_ctrl_red_FC = mean(mean(red_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
            mean_red_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(red_FC, na.rm = TRUE))))

## # A tibble: 9 × 7
##   mz_rt           mean_control_FC mean_beta_FC mean_red_FC mean_ctrl_beta_FC
##   <chr>                     <dbl>        <dbl>       <dbl>             <dbl>
## 1 244.908_0.706             1.07         1.08        1.08              1.00 
## 2 526.3144_2.683            1.15         1.23        1.09              1.07 
## 3 847.6455_10.298           0.913        0.857       0.927             0.938
## 4 907.6211_9.035            1.10         1.12        1.11              1.01 
## 5 915.6327_10.293           0.917        0.895       0.884             0.975
## 6 945.5095_6.552            1.05         0.941       0.965             0.898
## 7 945.6385_9.975            1.02         0.853       0.924             0.836
## 8 967.6322_9.976            0.980        0.873       0.903             0.891
## 9 991.6331_9.034            1.15         1.11        1.11              0.964
## # ℹ 2 more variables: mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>

Compile list

# combine cluster and FC info
tom_effect_list <- left_join(FC_tomato_effect, 
                             cluster_features,
                             by = "mz_rt") %>%
  select(mz_rt, mz, rt, Cluster_ID, Cluster_features, Cluster_size, everything())

# add in averages for each group + timepoint
tom_effect_list <-
  left_join(tom_effect_list,
            (df_for_stats %>%
               group_by(treatment_period, mz_rt) %>%
               summarize(mean_rel_abund = mean(rel_abund)) %>%
               pivot_wider(names_from = treatment_period, values_from = mean_rel_abund)),
            by = "mz_rt")

head(tom_effect_list, n=1)

## # A tibble: 1 × 19
##   mz_rt        mz    rt Cluster_ID Cluster_features Cluster_size mean_control_FC
##   <chr>     <dbl> <dbl> <chr>      <chr>                   <dbl>           <dbl>
## 1 244.908_…  245. 0.706 Cluster_2… 244.908_0.706;2…            2            1.07
## # ℹ 12 more variables: mean_beta_FC <dbl>, mean_red_FC <dbl>,
## #   mean_ctrl_beta_FC <dbl>, mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>,
## #   MPA <dbl>, beta_b1 <dbl>, beta_b3 <dbl>, control_b1 <dbl>,
## #   control_b3 <dbl>, red_b1 <dbl>, red_b3 <dbl>

Export list

write_csv(tom_effect_list, "effect lists/tomato-effect.csv")

Boxplots

# make comparison list for comparison tests
my_comparisons <- list( c("beta_b1", "beta_b3"), 
                        c("red_b1", "red_b3"),
                        c("control_b1", "control_b3") )

# subset df for uniquely significant features
justT_tom_effect_df <- df_for_stats_wide %>%
  dplyr::select(c(1:21),
                (all_of(overall_tomato_effect$mz_rt)))

# make tidy df
justT_tom_effect_df_tidy <- justT_tom_effect_df %>%
  pivot_longer(cols = 22:ncol(.),
               names_to = "mz_rt",
               values_to = "rel_abund_log2")

# fix factor levels for time points
justT_tom_effect_df_tidy$treatment_period <- factor(justT_tom_effect_df_tidy$treatment_period,
                              levels = c("control_b1", "control_b3", 
                                         "beta_b1", "beta_b3",
                                         "red_b1", "red_b3"))

# check
levels(justT_tom_effect_df_tidy$treatment_period)  

## [1] "control_b1" "control_b3" "beta_b1"    "beta_b3"    "red_b1"    
## [6] "red_b3"

justT_tom_effect_df_tidy$treatment <- factor(justT_tom_effect_df_tidy$treatment,
                              levels = c("control", "beta", "red"))

justT_tom_effect_df_tidy %>% 
  ggplot(aes(x = treatment_period, y = rel_abund_log2, fill = treatment_period)) +
  geom_boxplot() +
  scale_fill_manual(values = c("darkseagreen2", "darkgreen", 
                               "tan", "orangered2",
                               "lavenderblush3", "darkred"),
                    labels = c("pre control", "post control",
                               "pre beta", "post beta",
                               "pre lyc", "post lyc")) +
  scale_x_discrete(labels = c("", "", "", "", "", "")) +
  geom_line(aes(group = subject, colour = subject), size = 0.2) +
  theme_classic(base_size = 12, base_family = "sans") +
  facet_wrap(vars(mz_rt), scales = "free_y", ncol = 4) + 
  stat_compare_means(comparisons = my_comparisons, method = "t.test", paired = TRUE, p.adjust.method = "BH") +
  labs(x = "",
       y = "Log2 transformed relative abundance",
       title = "Tomato effect - significant when compared to control, also significant pre and post tomato",
       subtitle = "FDR adj. p-values from T-tests")

Beta effects

dim(sig_paired_beta_rmBG)

## [1] 167  20

Which features are significant in paired t test comparisons for beta, but not significant in the same direction for control?

Features that are significant in both pre- vs. post. beta AND post-beta vs. post-control

# select features that are only significant post beta v. post control comparison
overall_beta_effect <- semi_join(sig_paired_beta_rmBG,
                                 sig_beta_v_ctrl_t.test,
                                 by = "mz_rt")
dim(overall_beta_effect)

## [1]  5 20

overall_unique_beta_effect <- left_join(overall_beta_effect,
                                        sig_beta_v_red_t.test,
                                        by = "mz_rt")
dim(overall_unique_beta_effect)

## [1]  5 29

Summary stats

(FC_beta_effect <- df_for_stats %>%
  filter(mz_rt %in% overall_beta_effect$mz_rt) %>%
  select(subject, treatment_period, mz_rt, rel_abund) %>%
  group_by(mz_rt) %>%
  pivot_wider(names_from = treatment_period,
              values_from = rel_abund) %>%
  mutate(control_FC = control_b3/control_b1,
         beta_FC = beta_b3/beta_b1,
         red_FC = red_b3/red_b1) %>%
  summarize(mean_control_FC = mean(control_FC, na.rm = TRUE),
            mean_beta_FC = mean(beta_FC, na.rm = TRUE),
            mean_red_FC = mean(red_FC, na.rm = TRUE),
            mean_ctrl_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
            mean_ctrl_red_FC = mean(mean(red_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
            mean_red_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(red_FC, na.rm = TRUE))))

## # A tibble: 5 × 7
##   mz_rt           mean_control_FC mean_beta_FC mean_red_FC mean_ctrl_beta_FC
##   <chr>                     <dbl>        <dbl>       <dbl>             <dbl>
## 1 847.6455_10.298           0.913        0.857       0.927             0.938
## 2 896.6737_10.623           0.997        0.861       0.936             0.863
## 3 917.633_10.292            1.01         0.891       0.980             0.886
## 4 945.6385_9.975            1.02         0.853       0.924             0.836
## 5 967.6322_9.976            0.980        0.873       0.903             0.891
## # ℹ 2 more variables: mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>

Compile list

# combine cluster and FC info
beta_effect_list <- left_join(FC_beta_effect, 
                              cluster_features,
                              by = "mz_rt") %>%
  select(mz_rt, mz, rt, Cluster_ID, Cluster_features, Cluster_size, everything())

# add in averages for each group + timepoint
beta_effect_list <-
  left_join(beta_effect_list,
            (df_for_stats %>%
               group_by(treatment_period, mz_rt) %>%
               summarize(mean_rel_abund = mean(rel_abund)) %>%
               pivot_wider(names_from = treatment_period, values_from = mean_rel_abund)),
            by = "mz_rt")

head(beta_effect_list, n=1)

## # A tibble: 1 × 19
##   mz_rt        mz    rt Cluster_ID Cluster_features Cluster_size mean_control_FC
##   <chr>     <dbl> <dbl> <chr>      <chr>                   <dbl>           <dbl>
## 1 847.6455…  848.  10.3 847.6455_… 847.6455_10.298             1           0.913
## # ℹ 12 more variables: mean_beta_FC <dbl>, mean_red_FC <dbl>,
## #   mean_ctrl_beta_FC <dbl>, mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>,
## #   MPA <dbl>, beta_b1 <dbl>, beta_b3 <dbl>, control_b1 <dbl>,
## #   control_b3 <dbl>, red_b1 <dbl>, red_b3 <dbl>

Export list

write_csv(beta_effect_list, "effect lists/beta-effect.csv")

Boxplots

# subset df for uniquely significant features
justT_beta_effect_df <- df_for_stats_wide %>%
  dplyr::select(c(1:21),
                (all_of(overall_beta_effect$mz_rt)))

# make tidy df
justT_beta_effect_df_tidy <- justT_beta_effect_df %>%
  pivot_longer(cols = 22:ncol(.),
               names_to = "mz_rt",
               values_to = "rel_abund_log2")

# fix factor levels for time points
justT_beta_effect_df_tidy$treatment_period <- factor(justT_beta_effect_df_tidy$treatment_period,
                              levels = c("control_b1", "control_b3", 
                                         "beta_b1", "beta_b3",
                                         "red_b1", "red_b3"))

# check
levels(justT_beta_effect_df_tidy$treatment_period)  

## [1] "control_b1" "control_b3" "beta_b1"    "beta_b3"    "red_b1"    
## [6] "red_b3"

justT_beta_effect_df_tidy$treatment <- factor(justT_beta_effect_df_tidy$treatment,
                              levels = c("control", "beta", "red"))

justT_beta_effect_df_tidy %>% 
  ggplot(aes(x = treatment_period, y = rel_abund_log2, fill = treatment_period)) +
  geom_boxplot() +
  scale_fill_manual(values = c("darkseagreen2", "darkgreen", 
                               "tan", "orangered2",
                               "lavenderblush3", "darkred"),
                    labels = c("pre control", "post control",
                               "pre beta", "post beta",
                               "pre lyc", "post lyc")) +
  scale_x_discrete(labels = c("", "", "", "", "", "")) +
  geom_line(aes(group = subject, colour = subject), size = 0.2) +
  theme_classic(base_size = 12, base_family = "sans") +
  facet_wrap(vars(mz_rt), scales = "free_y", nrow = 3) + 
  stat_compare_means(comparisons = my_comparisons, method = "t.test", paired = TRUE, p.adjust.method = "BH") +
  labs(x = "",
       y = "Log2 transformed relative abundance",
       title = "High beta juice effect - significant when compared to control, also significant pre and post beta juice",
       subtitle = "FDR adj. p-values from T-tests")

Red effects

dim(sig_paired_red_rmBG)

## [1] 95 20

# select features that are only significant post red v. post control comparison

overall_red_effect <- semi_join(sig_paired_red_rmBG,
                                 sig_red_v_ctrl_t.test,
                                  by = "mz_rt")
dim(overall_red_effect)

## [1]  1 20

# select features that are only significant post tomato v. post control comparison

overall_unique_red_effect <- left_join(overall_red_effect,
                                        sig_beta_v_red_t.test,
                                        by = "mz_rt")
dim(overall_unique_red_effect)

## [1]  1 29

head(overall_unique_red_effect)

## # A tibble: 1 × 29
##   mz_rt  .y..red group1.red group2.red n1.red n2.red statistic.red df.red  p.red
##   <chr>  <chr>   <chr>      <chr>       <int>  <int>         <dbl>  <dbl>  <dbl>
## 1 945.6… rel_ab… b1         b3             12     12          2.33     11 0.0396
## # ℹ 20 more variables: p.signif.red <chr>, .y..ctrl <chr>, group1.ctrl <chr>,
## #   group2.ctrl <chr>, n1.ctrl <int>, n2.ctrl <int>, statistic.ctrl <dbl>,
## #   df.ctrl <dbl>, p.ctrl <dbl>, p.signif.ctrl <chr>, sign <dbl>, .y. <chr>,
## #   group1 <chr>, group2 <chr>, n1 <int>, n2 <int>, statistic <dbl>, df <dbl>,
## #   p <dbl>, p.signif <chr>

Summary stats

(FC_red_effect <- df_for_stats %>%
  filter(mz_rt %in% overall_red_effect$mz_rt) %>%
  select(subject, treatment_period, mz_rt, rel_abund) %>%
  group_by(mz_rt) %>%
  pivot_wider(names_from = treatment_period,
              values_from = rel_abund) %>%
  mutate(control_FC = control_b3/control_b1,
         beta_FC = beta_b3/beta_b1,
         red_FC = red_b3/red_b1) %>%
  summarize(mean_control_FC = mean(control_FC, na.rm = TRUE),
            mean_beta_FC = mean(beta_FC, na.rm = TRUE),
            mean_red_FC = mean(red_FC, na.rm = TRUE),
            mean_ctrl_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
            mean_ctrl_red_FC = mean(mean(red_FC, na.rm = TRUE)/mean(control_FC, na.rm = TRUE)),
            mean_red_beta_FC = mean(mean(beta_FC, na.rm = TRUE)/mean(red_FC, na.rm = TRUE))))

## # A tibble: 1 × 7
##   mz_rt          mean_control_FC mean_beta_FC mean_red_FC mean_ctrl_beta_FC
##   <chr>                    <dbl>        <dbl>       <dbl>             <dbl>
## 1 945.6385_9.975            1.02        0.853       0.924             0.836
## # ℹ 2 more variables: mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>

Compile list

# combine cluster and FC info
red_effect_list <- left_join(FC_red_effect, 
                             cluster_features,
                             by = "mz_rt") %>%
  select(mz_rt, mz, rt, Cluster_ID, Cluster_features, Cluster_size, everything())

# add in averages for each group + timepoint
red_effect_list <-
  left_join(red_effect_list,
            (df_for_stats %>%
               group_by(treatment_period, mz_rt) %>%
               summarize(mean_rel_abund = mean(rel_abund)) %>%
               pivot_wider(names_from = treatment_period, values_from = mean_rel_abund)),
            by = "mz_rt")

head(red_effect_list, n=1)

## # A tibble: 1 × 19
##   mz_rt        mz    rt Cluster_ID Cluster_features Cluster_size mean_control_FC
##   <chr>     <dbl> <dbl> <chr>      <chr>                   <dbl>           <dbl>
## 1 945.6385…  946.  9.98 945.6385_… 945.6385_9.975              1            1.02
## # ℹ 12 more variables: mean_beta_FC <dbl>, mean_red_FC <dbl>,
## #   mean_ctrl_beta_FC <dbl>, mean_ctrl_red_FC <dbl>, mean_red_beta_FC <dbl>,
## #   MPA <dbl>, beta_b1 <dbl>, beta_b3 <dbl>, control_b1 <dbl>,
## #   control_b3 <dbl>, red_b1 <dbl>, red_b3 <dbl>

Export list

write_csv(red_effect_list, "effect lists/red-effect.csv")

Boxplots

# subset df for uniquely significant features
justT_red_effect_df <- df_for_stats_wide %>%
  dplyr::select(c(1:21),
                (all_of(overall_unique_red_effect$mz_rt)))

# make tidy df
justT_red_effect_df_tidy <- justT_red_effect_df %>%
  pivot_longer(cols = 22:ncol(.),
               names_to = "mz_rt",
               values_to = "rel_abund_log2")

# fix factor levels for time points
justT_red_effect_df_tidy$treatment_period <- factor(justT_red_effect_df_tidy$treatment_period,
                              levels = c("control_b1", "control_b3", 
                                         "beta_b1", "beta_b3",
                                         "red_b1", "red_b3"))

# check
levels(justT_red_effect_df_tidy$treatment_period)  

## [1] "control_b1" "control_b3" "beta_b1"    "beta_b3"    "red_b1"    
## [6] "red_b3"

justT_red_effect_df_tidy$treatment <- factor(justT_red_effect_df_tidy$treatment,
                              levels = c("control", "beta", "red"))

justT_red_effect_df_tidy %>% 
  ggplot(aes(x = treatment_period, y = rel_abund_log2, fill = treatment_period)) +
  geom_boxplot() +
  scale_fill_manual(values = c("darkseagreen2", "darkgreen", 
                               "tan", "orangered2",
                               "lavenderblush3", "darkred"),
                    labels = c("pre control", "post control",
                               "pre beta", "post beta",
                               "pre lyc", "post lyc")) +
  scale_x_discrete(labels = c("", "", "", "", "", "")) +
  geom_line(aes(group = subject, colour = subject), size = 0.2) +
  theme_classic(base_size = 12, base_family = "sans") +
  facet_wrap(vars(mz_rt), scales = "free_y") + 
  stat_compare_means(comparisons = my_comparisons, method = "t.test", paired = TRUE, p.adjust.method = "BH") +
  labs(x = "",
       y = "Log2 transformed relative abundance",
       title = "High lyc effect - significant when compared to control, \nsignificant pre vs post red",
       subtitle = "FDR adj. p-values from T-tests")

Export

Metabolite df

write_csv(DC_imp_metabind_clust_log2_noQCs, "final-omics-df-lipidomics-neg.csv",col_names = TRUE)